Assigning gesture dictionaries

ABSTRACT

Techniques for assigning a gesture dictionary in a gesture-based system to a user comprise capturing data representative of a user in a physical space. In a gesture-based system, gestures may control aspects of a computing environment or application, where the gestures may be derived from a user&#39;s position or movement in a physical space. In an example embodiment, the system may monitor a user&#39;s gestures and select a particular gesture dictionary in response to the manner in which the user performs the gestures. The gesture dictionary may be assigned in real time with respect to the capture of the data representative of a user&#39;s gesture. The system may generate calibration tests for assigning a gesture dictionary. The system may track the user during a set of short gesture calibration tests and assign the gesture dictionary based on a compilation of the data captured that represents the user&#39;s gestures.

BACKGROUND

Many computing applications such as computer games, multimediaapplications, office applications or the like use controls to allowusers to manipulate characters or control other aspects of anapplication. Typically such controls are input using, for example,controllers, remotes, keyboards, mice, or the like. Unfortunately, suchcontrols can be difficult to learn, thus creating a barrier between auser and such applications. Furthermore, such controls may be differentthan actual actions for which the controls are used. For example, a gamecontrol that causes a game character to swing a baseball bat may be acombination of buttons and may not correspond to an actual motion ofswinging the baseball bat, or a control to reposition a view on ascreen, such as repositioning the view of a map in a map application,may be a selection of arrow buttons on a keyboard and may not correspondto the actual motion of the files.

A system of commands that are universal to all users inhibits a user'spersonalized experience with the system and/or application. Often, userswith fundamentally different preferences on how to issue commands areforced to use a unified input module for issuing such commands. However,simply allowing the user to adjust the definition of the command topersonalize each command for the user may be a laborious task and theuser may not remember the changes, especially when there are a largenumber of possible commands.

SUMMARY

Disclosed herein are techniques for assigning a gesture dictionary in agesture-based system to a user, where the gesture dictionary correspondsto captured data representative of the user's gestures. In agesture-based system, gestures may control aspects of a computingenvironment or application, where the gestures may be derived from auser's position or movement in a physical space. A gesture-based systemmay comprise any number or combination of capture devices, displaydevices, processors, input devices, etc. A capture device may capturedata representative of a user in a physical space. A display device maydisplay a visual representation of the user that corresponds to theuser's gestures. A processor may process the captured data to determinea user's gesture and translate the gesture into a control of an aspectof the gesture-based system.

In an example embodiment, the system may monitor a user's gestures andselect a particular gesture dictionary in response to the manner inwhich the user performs the gestures. The gesture dictionary may beassigned in real time with respect to the capture of the datarepresentative of a user's gesture. The system may track the user duringa set of short gesture calibration tests and assign the gesturedictionary based on a compilation of the data captured that representsthe user's gestures.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing Summary, as well as the following Detailed Description ofillustrative embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating theembodiments, there are shown in the drawings example constructions ofthe embodiments; however, the embodiments are not limited to thespecific methods and instrumentalities disclosed. In the drawings:

FIG. 1 illustrates an example embodiment of a target recognition,analysis, and tracking system with a user playing a game.

FIG. 2 illustrates an example embodiment of a gesture based system thatmay calibrate gesture tests and assign a gesture dictionary to a user.

FIG. 3A depicts an example flow diagram for a developing gesturedictionaries and developing gesture calibration tests

FIG. 3B depicts an example of dictionary families organized intoseparate hierarchies of parent and child dictionaries.

FIG. 3C depicts an example flow diagram for assigning or reassigning agesture dictionary to a user and calibrating the dictionary ifnecessary.

FIG. 3D depicts an example of a possible relationship that may existbetween two different dictionary families.

FIG. 4 illustrates an example embodiment of a capture device andcomputing environment that may be used in a target recognition,analysis, and tracking system.

FIG. 5A illustrates a skeletal mapping of a user that has been generatedfrom a target recognition, analysis, and tracking system such as thatshown in FIG. 3.

FIG. 5B illustrates further details of a gesture recognizer architecturesuch as that shown in FIG. 4.

FIG. 6 illustrates an example embodiment of a computing environment inwhich the techniques described herein may be embodied.

FIG. 7 illustrates another example embodiment of a computing environmentin which the techniques described herein may be embodied.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

Disclosed herein are techniques for assigning a gesture dictionary in agesture-based system to a user, where the gesture dictionary correspondsto captured data representative of the user's gestures. The subjectmatter of the disclosed embodiments is described with specificity tomeet statutory requirements. However, the description itself is notintended to limit the scope of this patent. Rather, the claimed subjectmatter might also be embodied in other ways, to include elements similarto the ones described in this document in conjunction with other presentor future technologies.

Embodiments are related to techniques for assigning one or more gesturedictionaries to a user based on an analysis of one more of the user'sgestures. A gesture may be derived from a user's position or motion inthe physical space and may include any user motion, dynamic or static,such as running, moving a finger, or a static pose. According to anexample embodiment, a capture device, such as a camera, may capturedata, such as image data, that is representative of the user'sgesture(s). A computer environment may be used to recognize and analyzethe gestures made by the user in the user's three-dimensional physicalspace such that the user's gestures may be interpreted to controlaspects of a system or application space. The computer environment maydisplay user feedback by mapping the user's gesture(s) to an avatar on ascreen.

A gesture-based system or application may have default gestureinformation for determining if a user is performing a particulargesture. For example, a system may have a gesture recognizer thatcompares captured data to a database of default gesture information suchas filters with default gesture parameters. The gesture recognizer maycompare data received by the capture device to the default gestureinformation and output a gesture. The output may include a confidencelevel that the output gesture was performed.

A gesture-based system may employ techniques for selecting a gesturedictionary for a user that is tailored to the manner in which the userperforms various gestures. The default gesture data may comprise aplurality of gesture dictionaries that are arranged in a hierarchicalmanner. A parent gesture set may be further defined by childdictionaries and the child dictionaries may be further defined by childdictionaries. Some gesture sets in a family of gesture dictionariesordered in a hierarchical manner may define the same gestures indifferent manners, where a particular gesture set may be a closer fit tothe user's performed gestures than another gesture set.

The system, methods, techniques, and components of assigning gesturedictionaries may be embodied in a multi-media console, such as a gamingconsole, or in any other computing environment in which it is desired todisplay a visual representation of a target, including, by way ofexample and without any intended limitation, satellite receivers, settop boxes, arcade games, personal computers (PCs), portable telephones,personal digital assistants (PDAs), and other hand-held devices.

FIG. 1 illustrates an example embodiment of a configuration of a targetrecognition, analysis, and tracking gesture-based system 10 that mayemploy the disclosed techniques for gesture personalization and gestureprofile roaming. In the example embodiment, a user 18 is playing abowling game. In an example embodiment, the system 10 may recognize,analyze, and/or track a human target such as the user 18. The system 10may gather information related to the user's motions, facialexpressions, body language, emotions, etc, in the physical space. Forexample, the system may identify and scan the human target 18. Thesystem 10 may use body posture recognition techniques to identify thebody type of the human target 18. The system 10 may identify the bodyparts of the user 18 and how they move.

As shown in FIG. 1, the target recognition, analysis, and trackingsystem 10 may include a computing environment 212. The computingenvironment 212 may be a multimedia console, a personal computer (PC), acellular device, a gaming system or console, a handheld computingdevice, a PDA, a music player, a cloud computer, a capture device, orthe like. According to an example embodiment, the computing environment212 may include hardware components and/or software components such thatthe computing environment 212 may be used to execute applications. Anapplication may be any program that operates or is executed by thecomputing environment including both gaming and non-gaming applications,such as a word processor, spreadsheet, media player, databaseapplication, computer game, video game, chat, forum, community, instantmessaging, or the like.

As shown in FIG. 1, the target recognition, analysis, and trackingsystem 10 may include a capture device 202. The capture device 202 maybe, for example, a camera that may be used to visually monitor one ormore users, such as the user 18, such that gestures performed by the oneor more users may be captured, analyzed, and tracked to perform one ormore controls or actions within an application. In the exampleembodiment shown in FIG. 1, a virtual object is a bowling ball and theuser moves in the three-dimensional physical space as if actuallyhandling the bowling ball. The user's gestures in the physical space cancontrol the bowling ball displayed on the screen 14. In exampleembodiments, the human target such as the user 18 may actually have aphysical object. In such embodiments, the user of an electronic game maybe holding the object such that the motions of the player and the objectmay be used to adjust and/or control parameters of the game. Forexample, the motion of a player holding a racket may be tracked andutilized for controlling an on-screen racket in an electronic sportsgame. In another example embodiment, the motion of a player holding anobject may be tracked and utilized for controlling an on-screen weaponin an electronic combat game.

According to one embodiment, the target recognition, analysis, andtracking system 10 may be connected to an audiovisual device 16 such asa television, a monitor, a high-definition television (HDTV), or thelike that may provide game or application visuals and/or audio to a usersuch as the user 18. For example, the computing environment 212 mayinclude a video adapter such as a graphics card and/or an audio adaptersuch as a sound card that may provide audiovisual signals associatedwith the game application, non-game application, or the like. Theaudiovisual device 16 may receive the audiovisual signals from thecomputing environment 212 and may then output the game or applicationvisuals and/or audio associated with the audiovisual signals to the user18. According to one embodiment, the audiovisual device 16 may beconnected to the computing environment 212 via, for example, an S-Videocable, a coaxial cable, an HDMI cable, a DVI cable, a VGA cable, or thelike.

As used herein, a computing environment may refer to a single computingdevice or to a computing system. The computing environment may includenon-computing components. As used herein, a computing system, computingdevice, computing environment, computer, processor, or other computingcomponent may be used interchangeably. For example, the computingenvironment may comprise the entire target recognition, analysis, andtracking system 10 shown in FIG. 1. The computing environment mayinclude the audiovisual device 16 and/or the capture device 202. Eitheror both of the exemplary audiovisual device 16 or capture device 202 maybe an entity separate but coupled to the computing environment or may bepart of the computing device that processes and displays, for example.Thus, computing environment may be a standalone capture devicecomprising a processor that can process the captured data.

As shown in FIG. 1, the target recognition, analysis, and trackingsystem 10 may be used to recognize, analyze, and/or track a human targetsuch as the user 18. For example, the user 18 may be tracked using thecapture device 202 such that the gestures of user 18 may be interpretedas controls that may be used to affect the application being executed bycomputer environment 212. Thus, according to one embodiment, the user 18may move his or her body to control the application. The system 10 maytrack the user's body and the motions made by the user's body, includinggestures that control aspects of the system, such as the application,operating system, or the like.

The system 10 may translate an input to a capture device 202 into ananimation, the input being representative of a user's motion, such thatthe animation is driven by that input. Thus, the user's motions may mapto a visual representation, such as an avatar, such that the user'smotions in the physical space are emulated by the avatar. The rate thatframes of image data are captured and displayed may determine the levelof continuity of the displayed motion of the visual representation.

FIG. 1 depicts an example embodiment of an application executing on thecomputing environment 212 that may be a bowling game that the user 18may be playing. In this example, the computing environment 212 may usethe audiovisual device 16 to provide a visual representation of abowling alley and bowling lanes to the user 18. The computingenvironment 212 may also use the audiovisual device 16 to provide avisual representation of a player avatar 19 that the user 18 may controlwith his or her movements. The computer environment 212 and the capturedevice 202 of the target recognition, analysis, and tracking system 10may be used to recognize and analyze the gestures made by the user 18 inthe user's three-dimensional physical space such that the user'sgestures may be interpreted to control the player avatar 19 in gamespace. For example, as shown in FIG. 1, the user 18 may make a bowlingmotion in a physical space to cause the player avatar 19 to make abowling motion in the game space. Other movements by the user 18 mayalso be interpreted as controls or actions, such as controls to walk,select a ball, position the avatar on the bowling lane, swing the ball,etc.

Multiple users can interact with each other from remote locations. Thecomputing environment 212 may use the audiovisual device 16 to providethe visual representation of an avatar that another user may controlwith his or her movements. For example, the visual representation ofanother bowler displayed on the audiovisual device 16 may berepresentative of another user, such as a second user in the physicalspace with the user, or a networked user in a second physical space.Similarly, an avatar may be displayed in non-gaming applications, suchas a word processing or spreadsheet document. Avatars may be displayedthat represent respective users that are remote to each other.

Gestures may be used in a video-game-specific context such as thebowling game example shown in FIG. 1. In another game example such as adriving game, various motions of the hands and feet may correspond tosteering a vehicle in a direction, shifting gears, accelerating, andbreaking. The player's gestures may be interpreted as controls thatcorrespond to actions other than controlling the avatar 19, such asgestures used for input in a general computing context. For instance,various motions of the user's 18 hands or other body parts may to end,pause, or save a game, select a level, view high scores, communicatewith a friend, etc.

While FIG. 1 depicts the user in a video-game-specific context, it iscontemplated that the target recognition, analysis, and tracking system10 may interpret target movements for controlling aspects of anoperating system and/or application that are outside the realm of games.Virtually any controllable aspect of an operating system and/orapplication may be controlled by movements of the target such as theuser 18. For example, the user's gestures may correspond to commonsystem wide tasks such as navigate up or down in a hierarchical list,open a file, close a file, and save a file. The user's gesture may becontrols applicable to an operating system, non-gaming aspects of agame, or a non-gaming application. For example, the user's gestures maybe interpreted as object manipulation, such as controlling a userinterface. For example, consider a user interface having blades or atabbed interface lined up vertically left to right, where the selectionof each blade or tab opens up the options for various controls withinthe application or the system. The system may identify the user's handgesture for movement of a tab, where the user's hand in the physicalspace is virtually aligned with a tab in the application space. Thegesture, including a pause, a grabbing motion, and then a sweep of thehand to the left, may be interpreted as the selection of a tab, and thenmoving it out of the way to open the next tab.

FIG. 2 illustrates an example system 200 that may implement techniquesfor determining the gesture dictionary that applies. System 200 mayinclude a computing environment 218. A computing environment may be amultimedia console, a personal computer (PC), a gaming system orconsole, a handheld computing device, a PDA, a mobile phone, a cloudcomputer, or the like. For example, the computing environment 218 may bea dedicated video game console, a central server or platform that hostsa plurality of clients, or a personal computing device, such as acellular telephone or a personal computer. The computing environment 218may comprise or otherwise be coupled to a capture device for receivingand processing data representative of a user and a user's gestures in aphysical space. For example, capture device 202, associated with user204 d, may capture data 270 representative of user 204 d in the user's204 d physical space 201. The capture device itself or another computingenvironment, such as computing environment 218, that receives thecaptured data may employ gesture recognition techniques to identifygestures from the captured data 270.

The users in this example are users 204 a, 204 b, 204 c, and 204 d,collectively users 204, but it is contemplated that any number of usersmay interact with the gesture-based system 200. For exemplary purposes,users 204 a, 204 b, and 204 c in this example are remote to thecomputing environment 218, user 204 d is local to computing environment218. One or more capture devices may be respectively associated witheach of the users 204 and capture data that is representative of eachuser in the user's respective physical space, with capture device 202representing an example of such capture device. The capture device maybe a depth camera or a plurality of cameras, for example, thatcommunicates with a single computing environment or with a plurality ofcomputing environments. The captured data 270 represents captured datacaptured separately for each of the users 204 or an aggregation ofcaptured data captured for any combination of users.

In this example, the computing environment 218 receives and processescaptured data 270, either remotely or locally, that represents each ofusers 204 a, 204 b, 204 c, and 204 d for gesture recognition. Thecomputing environment 218 may store the captured data in an inputgesture database 260. The computing environment 218 may process thecaptured data and identify the input gesture data, storing informationabout the input gesture data from the captured data in the input gesturedatabase 260. For example, capture device 202 may provide the captureddata 270 to the computing environment 218 via a network 250 or, if thecapture device 202 shares a local environment with computing environment218, via either the network 250 and/or a direct connection 203. Thecaptured data 270 associated with remote users 204 a, 204 b, and 204 cmay be captured by a remote capture device and provided to the computingenvironment 218 via the network 250.

As described, the capture device, such as capture device 202, andcomputing environment 218 that may communicate with other systemcomponents via a network 250. A network 250 may include, for example, anintranet, an internet, the Internet, a personal area network (PAN), acampus area network (CAN), a local area network (LAN), a wide areanetwork (WAN), a computer network, a gaming network, or the like. Thenetwork 250 may also represent the technology that connects individualdevices in the network, such as optical fibre, a public switchedtelephone network (PSTN), a cellular telephone network, a global Telexnetwork, wireless LAN, Ethernet, power line communications, or the like.Computing environments may be connected together by wired or wirelesssystems, by local networks or widely distributed networks. Anyappropriate wireless interface can be utilized for networkcommunications. For example, the wireless link can be in accordance withthe following protocols: GSM, CDMA, UMTS, LTE, WIMAX, WIFI, ZIGBEE, or acombination thereof. A network may include cloud or cloud computing. Acloud infrastructure, for example, may include a multitude of servicesdelivered through data centers and built on servers. The services may beaccessible anywhere that provides access to the networkinginfrastructure. The cloud may appear to be a single point of access tothe user and the infrastructure may not be visible to a client.

In this example, computing environment 218 is shown executingapplication 227. An application 227 may be any program that operates oris executed by the computing environment including both gaming andnon-gaming applications, such as a word processor, spreadsheet, mediaplayer, database application, computer game, video game, chat, forum,community, instant messaging, or the like.

The computing environment may comprise modules for processing gesturedata to develop gesture sets and identify calibration tests for matchinga gesture dictionary to a user. For example, the computing environment218 may comprise a gesture set identification module 228 the developsgesture sets and identifies tests for assigning a gesture dictionary.FIG. 3A depicts an example flow diagram for developing gesture sets andidentifying calibration tests that may be performed by the gesture setidentification module 228. The system may create gesture sets ordictionaries at 302. A gesture, either alone or in combination withother exemplary gestures, uniquely identifies a gesture dictionary. Asdescribed in more detail below, the gesture dictionaries may behierarchically organized at 322. The system may select exemplarygestures for each gesture dictionary at 324 to be used in gesturecalibration tests at 326. For example, the system may select a “jump”gesture or a “open file” gesture from one or more gesture dictionaries.As described in more detail below, the system may use a selectedexemplary gesture for gesture calibration tests for assigning a gesturedictionary to a user at 326.

With respect to the organization of the dictionaries developed, wherethere may be any number of dictionaries developed, these dictionariesmay be hierarchically organized. Referring to the example shown in FIG.3B, for example, there may be a Dictionary A 302 and a Dictionary B,each of which corresponds to a fundamentally different gesture set. Eachof these dictionaries may have a family of related dictionaries thatrepresent additional gestures or more specific gestures. For example,the Dictionary A 302 family may have a parent dictionary, Dictionary A,and Dictionary A 302 may have a child gesture set A.1.c 306 c ParentDictionary A 302 may comprise gestures that are applicable to a scenarioand a child gesture set may comprise gestures that either supplement ormodify the gestures in parent Dictionary A. For example, parentDictionary A 302 may comprise gestures that are applicable system-wideand a child gesture set may comprise gestures that are also applicablesystem-wide but either supplement to or modify the gestures inDictionary A.

If a dictionary is hierarchically organized, the gesture dictionary maybe unique with respect to peer dictionaries at the same hierarchicaldepth. Thus, more than one dictionary may share the same parentdictionary, but at the child level, the children dictionaries may beunique with respect to each other. For example, in the example above,Dictionary A.1 may have a gesture for zooming in that involves two armsthat start separated and then come together such that the hands meet,whereas Dictionary B.1 304 a may have a gesture for zooming in thatinvolves two fingers pinching.

A family of related dictionaries may comprise any number of childdictionaries and child dictionaries of those child dictionaries, etc.For example, Dictionary A 302 has a child Dictionary A.1, and DictionaryA.1 has children dictionaries A.1.c 306 ca 306 a, A.1.c 306 cb 306 b,and A.1.c 306 cc. Each dictionary may apply in a particular situation,or a combination of dictionaries may be applicable. For example,Dictionary A 302 may represent the parent of a family of large limbgestures, Dictionary A.1 303 a may comprise gestures for large gesturescomprising arm motion and Dictionary A.2 303 b may comprise gestures forlarge gestures comprising leg motion. Some gestures may overlap withboth Dictionary A.1 303 a and Dictionary A.2 303 b, such as gesturescomprising both arm and leg motions. Within Dictionary A.1, specific armgestures may be defined by Dictionary A.1a, A.1b, and A.1c. Theparticular cluster of dictionaries that are assigned to a user,therefore, may be tailored to identifiers that associate with the user.The number of identifiers may correspond to a more tailored cluster ofdictionaries.

An identifier associated with each dictionary may further narrow theapplication of the dictionary. For example, consider the Dictionary B304 family. Dictionary B 304 may represent the gesture dictionaries thatapply in a particular gaming application. Dictionary B 304 comprisesfour dictionary children, B.1 304 a, B.2 304 b, B.3 304 c, B.4 304 d.Each of these may comprise gestures for the gaming application, and theidentifier may further define when each child dictionary applies. Forexample, the identifier x associated with Dictionary B.1 304 a mayidentify that the gesture Dictionary B.1 304 a applies for left handedusers; identifier y associated with Dictionary B.4 may associated thedictionary with right handed users, and identifier z associated withDictionary B.3 304 c may associate the dictionary with a particularlocation (e.g., a certain country or state). As described in more detailwith respect to FIG. 3D, dictionaries may overlap or share gestures. Forexample, Dictionary B.3 may comprise gestures applicable to a particularcountry, but may have gestures that also apply to right handed users.Thus, Dictionary B.3 and Dictionary B.4 may have overlapping gestures.

The options for the gesture dictionary or combination of gesturedictionaries that apply enables the system to more intuitively andeffectively recognize gestures for a particular mental model of a user.As described in more detail below, an identifier may be associated witheach gesture dictionary, where the identifier is indicative of a featureof the environment or the user that identifies when the dictionaryapplies. Thus, the system may use the identifier to assist in assigninga gesture dictionary to a user.

Referring back to FIG. 2, the computing environment may store thegenerated dictionaries of gesture data and any identifiers of eachgesture and/or relationship between gestures in the gesture set database261. For example, the sets of gesture data may be stored as a structuredcollection of records and/or data associated with the sets of gesturedata identified and/or modified by the input gesture database 260 andthe gesture set database 261. The information in the gesture dictionarydatabase 261 may be structured to enable a person or program to extractdesired information to share information about the user. While describedas a database, it is contemplated that the gesture dictionary database261 may take any form of data storage, including a storage module,device, or memory, for example. The gesture dictionary database 261 maybe provided as a database management system, an object-oriented databasemanagement system, a relational database management system (e.g. DB2,Access, etc), a file system, or another conventional database package.Further, the database 261 can be accessed via a Structure Query Language(SQL), or other tools known to one of ordinary skill in the art.

The gesture set database 261 may include dictionaries of standardgesture data available for incorporating into the gesture-based system.Gesture data or sets of gesture data used for gesture recognition isalso referred to herein as gesture recognition data, where the termsgesture data, gesture set, and gesture recognition data may be usedinterchangeably. The gesture recognition data may serve as a dictionarythat enables the translation of a user's gestures detected by a capturedevice into an action to be executed by any computing device to controlan aspect of the gesture-based system.

It is noted that gestures may include dynamic or static movement as wellas voice commands, and may be performed to control aspects of the systemor an executing application. A gesture may also comprise input derivedfrom the combination of user position or movement captured by a capturedevice (herein “user position data” or “position data”) in conjunctionwith input derived from another source, such as the press of a button ona controller, or the position of an object in a scene in which the useris captured (herein “additional data”). The system may receivecontroller input from a wired or wireless controller. The user maysupplement the motions or poses that he makes with his body that arecaptured by capture device 202 with input via the buttons, joysticks orother pads of a controller. For instance, while playing a first-personshooter game, user 204 d may point with his arm at a location displayedby the computing environment 218. He may further press a button oncontroller at substantially the same time. Computing device 212 mayrecognize the user pointing while pressing a button as a “dischargefirearm” gesture.

Thus, a computing environment may not only include a gesture interfacebut may process instructions configured to use tactile based (inputrelying on touch) user input. For example, application 227 may comprisesuch instructions, where application 227 may be any type of program suchas operating system, word processor, videogame, etc. In an embodimentwhere application 227 is an operating system, the operating system caninclude input output drivers such as mouse drivers, keyboard drivers, acapture device driver, and other I/O drivers such as, for example, touchscreen drivers, microphone drivers, videogame controller drivers, or anyother human interface device drivers. In an embodiment where application227 is an application such as a web-browser, a word processor, a pictureediting program, etc, application 227 can include executableinstructions that request one or more threads to handle and process userinput. In this example, application 227 may rely on mouse drivers,keyboard drivers, and other I/O drivers installed on the operatingsystem and OS code to supply it with user interface messages.

Packages of standard gesture recognition data may be available forincorporating into the gesture-based system. Enabling dictionaries ofstandard gestures allows application developers to employ gesturerecognition techniques into their applications during development basedon a standard set for commonality. A gesture set database 261 maycomprise a plurality of gesture recognition data. Where gestures arecomplementary with each other, the gesture data may be grouped intogesture sets, such as into the family of dictionaries depicted in FIG.3B. These gesture sets may be provided to applications for use by agesture recognizer engine. An application may utilize one or moregesture sets. Thus, the gestures associated with the gesture-basedsystem, such as an application executing on a computing environment inthe gesture-based system, may be determined by the gesture sets that areassigned or available in each scenario.

A gesture set (or sets), such as a default gesture set, may be providedwith an application 227 or come packaged with another component of thegesture-based system, such as delivered with the computing environment218 or incorporated into the operating system. A standardized defaultgesture set (or sets) may apply universally for all users. For example,a set of gestures such as Dictionary A 302 described with respect toFIG. 3B may be universally applicable across operating systems and applysystem-wide such that a user may perform certain commands via gesturesat any time while interacting with the system, despite variations in thesystem (such as a different application executing on the system).

As described herein, input gesture data may be analyzed to assign adictionary that enables the translation of input gesture data comprisinggestures, detected by a capture device, into an action to be executed bythe gesture-based system to control an aspect of the gesture-basedsystem. The computing environment 218 may comprise a module foranalyzing or manipulating the input gesture data received to assign agesture dictionary to a user. For example, the computing environment 218may comprise a gesture set calibration & assignment module 229. Thegesture set calibration & assignment module 229 may analyze ormanipulate the input gesture data received to identify the gesturedictionary. By observing a user for a brief time, the system candetermine what gesture dictionary the system should apply. For example,the input gesture database 260 may contain an inventory of input gesturedata, such as gesture data received from users interacting with thesystem. The computing environment may store or otherwise have access toinput gesture database 260. The inventory of gesture data in the gesturedatabase 260 may a structured collection of records and/or dataassociated with the gesture data captured or received by the computingenvironment 218. The system can analyze information from the inputgesture database for a particular user to determine the gesturedictionary that applies for the particular user. As noted above, agesture may comprise inputs such as voice commands or tactile input.Thus, a gesture dictionary that applies may be based on inputs otherthan those derived from a user's position or motion in the physicalspace. For example, a dictionary may be assigned that is associated withthe way a person speaks or a particular language of the user. Thedictionary assigned may be based on a combination of input types. Forexample, a user may tend to use voice commands and a user's motion inphysical space, or a user's position in the physical space and a tactileinput. The dictionary assigned may be best fit to the manner in which auser gestures.

The system may cluster users for assisting in gesture dictionaryassignment. For example, the system may correlate the user's inputgesture data to the input gesture data of another user, recognizingsimilarities between various features of the input gesture data of bothusers. The system may select gesture dictionaries for assignment to auser based on the dictionaries assigned to another user, where the inputgesture data between users comprises similarities. Thus, the system maycorrelate the input gesture data to a gesture dictionary by correlatingthe input gesture data of a first user to an input gesture data of asecond user, and assigning a gesture dictionary assigned to the seconduser to the first user. Consider an example of a user that is playing adriving game, and is motioning in the physical space to simulate theholding of and the motion of a steering wheel. A first user may standupright and move his or her hands in a steady, circular motion. A seconduser may move their hands to the left and right without rotation. Athird user may steer with both hands on the side and rotate quicklyrather than making a smooth motion. The first, second, and third usermay each represent the gestures performed by a plurality of users,creating a first, second, and third cluster of input gesture data typesfor gesturing with a steering wheel. Each cluster may be represented byan identifier and a corresponding gesture dictionary. The system mayassociate an identifier x with the first user's gesture related to thesteering wheel, an identifier y with the second user's gestures relatedto the steering wheel, and identifier z with the third gesture type.When the system captures data representative of a user, the system mayidentify the user's motion as either closer to identifier x, y, or z,and cluster the user with the best fit cluster. Any number of users maybe assigned a dictionary based on such clustering of gesture data.

The cluster of gesture data may be defined by an identifier that isbroadly defined as the gesture parameters common to the cluster. Inanother example, a more specific identifier may be identified. Forexample, the third cluster described above may be common to users in aparticular region or country. Thus, the identifier may be defined by thelocation rather than the gesture data. Thus, a user that enters into thegesture-based system may be associated with a gesture dictionary basedon the user's location, as identified by the system and correlated tothe appropriate identifier. Over time, the system may further assigngesture dictionaries to a user as more features of the user areidentified. For example, the user may be assigned to a particularcluster of gesture recognition data for being a left-handed user.However, over time, the system may recognize that the user isleft-handed, typically performs small scale gestures, and speaks in asoft tone. Thus, the gesture dictionaries assigned to the user may betailored to those dictionaries associated with identifiers representingthese traits. It may be that a particular identifier does not exist fora “soft tone,” for example. The system may correlate traits of aparticular user to those of another user and cluster users based on thesimilarities between users, thus assigning users within a cluster to thesame gesture dictionaries.

An identifier may be associated with a gesture dictionary. For example,gesture set identification module 228 may associate one or a combinationof identifiers to a gesture set. As shown in FIG. 3B, for example,Dictionary A.1 303 a is associated with identifier x. An identifier,such as identifier x, may be any feature of the scenario that isidentifiable by the system. The gesture set identification module 228may associate the identifier with a gesture dictionary during generationof the dictionary, and the identifier may change as a gesture dictionaryevolves.

An identifier associated with a gesture dictionary may assist toidentify the applicability of a gesture set for gesture recognition.Consider if a dictionary is associated with an identifier where theidentifier is a location. The system may identify a location via GPS orvia an IP address, where a location may be an identifier. The system mayidentify a user may by capturing data representative of the user andemploying body/facial recognition techniques, where a particular user ordetected features of a user may be an identifier. The system mayidentify the operating system or an application loaded by an analysis ofthe hardware/software configuration, such as by identifying the serialnumbers of components labeled during installation, where an operatingsystem or an application may be an identifier.

In another example, the identifier associated with a dictionary maydefine a specific user, a type of user, or a feature of a user (e.g.,geographically, demographically, linguistically, culturally, etc.). Thesystem may track users, identify a user or group of users, or identifyfeatures of a user, and if the system identifies a correlation of thescenario to the identifier, the gesture dictionary associated with theidentifier may be assigned.

In yet another example, the identifier may correspond to the basis forthe needed updates. For example, a particular cultural context may bethe reason why a certain gesture fails for a group of users (e.g., agesture for powering the system off that comprises a waving gesture,where a waving gesture may be a derogatory motion within a particularcultural context). The common feature of the users to which an evolvedgesture set may apply, that does not use a waving gesture, may be aparticular culture—the cause of the need for evolving the gesture data.

While the above examples provide specific examples for an identifier, itis noted that the identifier may be any system-identifiable feature ofthe scenario. For example, the identifier may be at least one of anoperating system, an application, a user, a feature of a user, alocation, a type of application, a hardware configuration, a softwareconfiguration, a culture, current user, geography, demography,linguistic, culture, or a style. The system may correlate a scenario tothe identifier by analyzing captured data (e.g., identifying a user fromthe captured data or identifying a culture based on a user's gestures),or via other inputs by a user or a component in the system (e.g., theuser may indicate a location by selecting a location from a menu, or thesystem may provide details of an existing hardware configuration, alocation device may provide a location, etc). Upon recognition of theidentifier, the system may select the gesture set that is associatedwith the identifier and implement the gesture set for gesturerecognition.

FIG. 3C depicts an example flow diagram for a method of assigning agesture dictionary. In an example, the system may use calibration testsdefined by the gesture identification module 228 for matching a userwith a gesture dictionary. In another example, the gesture-based systemmay recognize a failure of a user to satisfy gesture data in an assigneddictionary, thereby resulting in a failure to issue a control or commendto the system via the gesture. The recognition of a level of failure mayresult in an assignment or a reassignment of a gesture dictionary. Forexample, a default gesture dictionary may be assigned as part ofcalibration and the system may recognize child dictionaries that shouldbe assigned that may be appropriate to supplement the defaultdictionary.

The system may track the user explicitly or passively for assigning agesture dictionary. For example, the system may request that the userperform a series of gestures. The system may recognize the failure basedon explicitly requested feedback, such as by requesting a user toperform a gesture and comparing the captured gesture data to the storedgesture data. In an example embodiment, the system may perform a gesturecalibration by presenting the user with feedback indicative of areceived instruction and ask the user to perform the correspondinggesture. A calibration procedure may take place upon initialization ofthe system, an application, or when a new user is identified. From thecaptured data, the system can identify the effectiveness of the gesturesand how well the user performs each in light of the stored gesture data.

The system may also recognize the failure through passive tracking ofthe user, where the user may or may not know the system is capturinggesture data for purposes of analyzing of the effectiveness of thestored gesture data. While the user is interacting with the system, thesystem may track gestures performed by the user for the purpose ofanalyzing the effectiveness of the gesture data. In an example, thesystem may expect a particular gesture but fail to recognize the gesturefrom the user's gesture data. For example, if a user is interacting witha baseball game application or a word processing document, the systemmay expect a particular gesture based on the circumstances, such as ahitting gesture when the user's player is at bat in the baseball gameapplication or a save gesture when the user closes a word processingdocument.

FIG. 3C illustrates examples of instructions given to a user at 333 and336. For example, at 333 the system may provide an instruction such as“run in place” or “wave good-bye.” The extent of instructions may rangefrom very specific to very broad. For example, the instructions given bythe system at 333 may be broad such as an audio command to perform azoom operation (e.g., “demonstrate an action that corresponds to a zoomfunction”) without a display or specific example demonstrated. The usermay respond by performing an appropriate gesture that corresponds to thevisual command. The instructions may be even more specific, such as, forexample, at 336 the system may display a picture, demonstrate a zoomoperation on the picture, and then request the user to perform thepreferred gesture that will result in the same zoom operation. The usermay motion or pose in a manner that is intuitive or preferred by theuser to result in a zoom operation such as the one demonstrated by thesystem. The user may use any motion or pose desired for that user.

Alternatively, the system may initiate the gesture assignment process bygathering information about a user's gesture without requesting explicitinput by the user. For example, at 337, when the system receives datarepresentative of a physical space, with or without the instructionsgiven by the system such as at 333 or 336, the system may identify auser's gestures from the received data. With passive recognition, thesystem may perform gesture dictionary assignment without requestingexplicit input from the user. Thus, the system may recognize gestures at338 via explicit or passive data capture.

The system may manipulate the circumstances of the gesture-based systemsuch that the natural action for the user includes the training data thesystem is looking to collect. The manipulation of an output of thegesture-based system to the user can be monitored and used to elicit aparticular gesture, where the system can compile the resulting inputgesture data. For example, the system may be compiling information for auser's gesture related to fighting in a game application. To solicit aparticular gesture, during game play a single monster might appear inisolation—by context of the game play the appearance of the monster mayprompt the user to perform a specific gesture to eradicate the monster,such as a gesture for poking the monster in the eyes. In anotherexample, the system may be compiling information for platform gestures,such as powering on a component of the system. The system may cause acomponent to power down and then track the user, compiling input gesturedata to evaluate the user's gesture for powering the component on. Thus,the system can elicit certain gestures from the user by manipulatingcertain circumstances.

At 343, the system may recognize an identifier based on the identity ofthe user or an identifier otherwise recognized from the received data(e.g., an operating system, a system location, a user location, anapplication, etc). At 345, the system may identify a user in thephysical space such as, for example, reading a user profile or analyzingthe captured data to identify the user. At 347, the system may assign adictionary at 347 to the user based on the identifier or identity of theuser.

If explicit instructions are provided to the user, such as the exampleinstructions at 333 or 336, the system may capture data representativeof the user's gesture at 337 and detect the performed gesture at 338.The system may record the gesture information to define the parametersfor the user's gesture, such as for a user's zoom operation. At 339, thesystem may evaluate whether the user's gesture matches a gesture from adictionary, such as an exemplary gesture selected for purposes ofcalibration (as described above). If so, the system may assign thedictionary or related dictionaries to the user that includes the gesturethat corresponds to the user's performed gesture. The gesture dictionaryassigned may be based on how the user performed the gesture. The gesturerecognition data to be implemented for that gesture thereafter maytherefore correspond to the captured data.

The system may assign to a user an entire family of dictionaries, asubset of a family of dictionaries, or one dictionary within a family,and may assign to the user dictionaries from multiple families forgesture recognition when the circumstances are appropriate. For example,with respect to the organization of dictionaries shown in FIG. 3B, thesystem may recognize that the user's gestures correspond to those inDictionaries A and A.1, but not to any of the children dictionaries ofA.1, and also recognize that the user's gestures correspond to adictionary in the dictionary B 304 family.

Assume for purposes of an example that the Dictionary A 302 familydepicted in FIG. 3B comprises gestures that are applicable system-wideand the Dictionary B 304 family may represent gesture sets that areapplication-specific. Dictionary A 302 and B 304 may be related in thatan application corresponding to at least one of the gesture sets in theDictionary B 304 family executes on the operating system associated withthe Dictionary B 304 family. In another example, Dictionary A 302 and B304 may be distinct dictionary families that are not related. Forexample, both Dictionary A 302 and B 304 may both apply system-wide, butthe gesture sets may apply in distinct scenarios or at distinct timesbased on the scale of the gestures. For example, Dictionary A 302 mayrepresent a set of gestures that involve large movements of a user'slimbs. Dictionary A.1 303 a may be related to Dictionary A 302 in thatDictionary A.1 303 a involves large movements of the arms whereasDictionary A.2 303 b involves large movements of the legs. DictionaryA.1 303 a.a, A.1 303 a.b, and A.1 303 a.c may be different gestures thatmake use of the arms and Dictionary A.2 303 b.a and A.2 303 b.b maycomprise gestures that make use of the user's legs. Dictionary B 304 mayrepresent a set of gestures that involve primarily finger motions, suchas pinching and pointing. Dictionary B.1 304 a may involve a set ofgestures that assigns different meaning based on the number of fingers auser extends for a particular gesture. As the example demonstrates, thesub-dictionaries may be a more specific definition for the gesturesand/or additional gestures that are defined more specifically than thosein the parent dictionary.

An identifier may assist in gesture dictionary assignment orreassignment. For example, an identifier may be associated with agesture dictionary, either during development or as a result ofadaptations made to the dictionary. In the former example, a developermay designate identifiers during development. For example, the developermay recognize a difference in gestures between cultures, and adictionary may correspond to each culture where the identifier is theindication of the culture associated with a particular dictionary. Inthe latter example, where the identifier may be associated with adictionary that has been modified, some systems and/or applications areadapted to modify the definitions of gesture data based on a number ofreasons. For example, the system may recognize that a user has aspecific method for performing a gesture, or the system may aggregategesture data from multiple users and refine the gesture data tocorrespond to gesture data actually captured from users.

The identifier may be associated with an exemplary gesture selected forcalibration purposes. For example, during development an exemplarygesture may be selected for use in assigning gesture dictionaries. Thesystem may request that a user perform the exemplary gesture (orgestures), and compare the received data representative of the user'sgestures to the exemplary gesture data. The results may assist in thedetermination of gesture dictionary assignment by comparing the inputgesture data to gesture recognition data available for the gesture. Forexample, the exemplary gesture may be a jump gesture. The system maytrack the user as the user performs a “jump” gesture and determine theagility of the user, the size of the user, the height of the user, orthe energy of the user. The system may assign a gesture dictionary basedon these traits. For example, if the user does not move very quickly anddoes not jump very high, the system may assign a dictionary thatrepresents gestures comprising less movement. However, if the user jumpsand is very active, as identified by the system from the gesture data,the system may assign a dictionary that represents the same gestures butyet, with more movement involved.

In another example, the system may request that the user perform a pointand select gesture. The system may prompt the user to perform themotion, pose, or give a verbal command that he or she associates with“point and select.” The system may not only compare the gesture datawith the available gesture dictionaries to determine which, if any,represents gesture data similar to the received data representative ofthe user, but the system may determine other qualities of the user, suchas handedness, or gender. The system may use the information to assignthe dictionary that best corresponds to the user.

When the identifier corresponds to the circumstances of a particularscenario, the gesture set may be implemented. In this example, childdictionary A.1 303 a is associated with identifier x. The identifier xmay be any number of identifying features for identifying the scenarioor a group of users to which a gesture set applies. Initially and/orover time, the system may recognize circumstances of the scenario thatcorrelate to an identifier associated with a gesture dictionary, whetherit is a recognition of a specific user, or a circumstance of theenvironment, such as an operating system, etc. For example, over timethe system may recognize that a user is left-handed and assign orreassign dictionaries based on this recognition. In another example, thesystem may recognize the switch to a new application and assign adictionary to a user based on an entirely different set of gestures.

The identifier may broadly associate the gesture set to a type ofoperating system or application or the identifier may be more specificsuch as a particular user or a specific component used in the system.For example, an identifier may be based on a feature of a user. Userdata may have formed a basis for defining gesture recognition in agesture dictionary during development. The identifier may be a commonfeature among the users from which gesture data was initially generated.The system may recognize the presence of a circumstance that correlatesto an identifier and assign the gesture dictionary associated with theidentifier. For example, the identifier may be a handedness of the user,such as right-handed or left-handed. When the user's handedness isrecognized by the system, the system may assign the dictionary to theuser that is associated with the user's handedness.

The identifier may be a circumstance of the environment, such as alocation. Then, the system may recognize that a user is interacting withthe system from within the location and assign the corresponding gesturedictionary. The gesture-based system may identify the country based onan identity of the location of the system or the user. For example, thegesture-based system may be programmed with location-based systemsoftware that can utilize location-based services (e.g., GPS) todetermine the location of the components. In another example embodiment,the system identifies an IP address associated with the user's computingenvironment and uses the IP address to determine the location of theuser. In another example, the user may select a country and thegesture-based system may store the user's selection in memory.

The system can access a user profile or track the user to identifyfeatures of the user, such as a user's skill level or range. The systemcan compare the information about the user against the identifiersassociated with any number of dictionaries, and assign the gesturedictionary that corresponds to the information. For example, referringto FIG. 3B, Dictionary A 302 may correspond to a particular application.FIG. 3B depicts two example child dictionaries to Dictionary A 302,Dictionary A.1 303 a and A.2 303 b. Dictionary A 302 may define ageneral set of gestures, such as those that pertain to a particularapplication. Dictionary A.1 303 a's child dictionaries may correspond toa particular skill level as identified by the identifier x. Thus,Dictionary A.1 303 a and Dictionary A.2 303 b may each correspond to adifferent skill level as identified by the dictionary's respectiveidentifier, x or y. For example, identifier x may be ‘beginner’ andidentifier y may be ‘advanced.’ In an example, if a user selects a‘beginner’ mode upon startup of the system, the system can assignDictionary A.1 303 a and any child dictionaries of Dictionary A.1 303 athat are applicable to the user based on that identifier. In anotherexample, the user may have a user profile that indicates the user'sskill level, or the system may track the user and determine, bycomparing the user's input gesture data to gesture data in the gesturedictionaries, that the user's gestures correspond to a beginner skilllevel.

The identifier associated with a gesture set may be based on a culturalcontext. For example, the gesture set in Dictionary A 302 may beapplicable system-wide and apply as a default when a system is poweredon. The contents of the Dictionary A 302 may be pre-packaged as part ofthe platform that supports gestures. The gestures in Dictionary A.1 303a may supplement or modify the gestures in Dictionary A 302 tocorrespond to the particular culture. For example, Dictionary A.1 303 ais shown associated with identifier x. The identifier x may identify aparticular location or region in a country that is indicative of acultural context. Consider an example of a “close file” gesture defineduniversally in Dictionary A, where Dictionary A 302 defines gestures aspart of a default gesture set provided with a platform that supportsgestures. The “close file” in the default gesture set in Dictionary A302 may comprise a wave motion with one hand, corresponding intuitivelyto a motion that means good-bye to a group of users. However, a wavingmotion does not correspond intuitively to mean good-bye within certaincultures. In a first culture, for example, a waving motion may have aderogatory meaning.

Dictionary A.1 303 a may be implemented upon recognition of theidentifier (i.e., the cultural context) and supplement or modify thegestures in parent Dictionary A. In this example Dictionary A.1 maymodify the “close file” gesture to correspond to a motion that meansgood-bye to the users within the region identified by identifier x, suchas to a motion that makes better sense to the culture of users.

As described above, in another example embodiment, the identifier may berelated to other circumstances of the gesture based system, such as thetype of operating system or type of application executing duringcompilation of the gesture data. For example, a set of gestures mayfunction across varying operating systems and/or applications, theidentifier may assist in determining under which circumstances aparticular gesture set applies. The identifier, for example, mayassociate a gesture set with a specific category of applications. Forexample, a gesture set applicable for productivity type applications maybe universally applied for productivity scenarios, while another gestureset applicable for game applications may be universally applied for thegame applications. Thus, each of the sets of gesture data may applyuniversally within respective contexts. Further, a set of gestures maybe applicable based on one or any combination of identifiers. Or forexample, a gesture set may be generated for a certain type of userexecuting a certain type of application, such as a user in a particularregion gesturing to control a game application.

In another example embodiment, the identifier is a style feature, suchas a style classifier. The system can classify styles of gestures (maybesome users are very precise, some are sloppy, some use more hand, someuse less body, etc), and the system could compile data that correlatesto the particular style, noticing that they fit one of the styles moreclosely than the default. It is likely that at least a subset of userswithin the users of the system will benefit from a gesture set evolvedbased on a compilation of users with a similar style. The style may beapplicable to such things as a user's handwriting for generating orevolving gestures based on the handwriting of a plurality of users.

As shown by the example hierarchy of dictionaries shown in FIG. 3B,dictionaries may be distinct families or may be related in some manner.It is noted that reference to a dictionary herein contemplates adictionary, a combination of dictionaries, a family of dictionaries suchas those hierarchically organized in FIG. 3B, or any other collection ofrelated gestures. The term dictionary, set, package, or similar termreferring to the collection of related gestures is used interchangeably.Further, related gestures may be related if they are defined similarly(e.g., similar parameters). However, gestures may be related whetherthey have similar definitions if they are in any manner related to eachother, such as applicable to the same application, operating system, oruser, or if the gestures are assigned to the same user.

FIG. 3D depicts an example of a possible relationship that may existbetween the family of Dictionary A 302 to the family of Dictionary B. Inthis example, identifiers are used to indicate the relationship betweenthe cluster of dictionaries in a family and between families ofdictionaries. However, it is contemplated that any method of relatingthe dictionaries to each other or distinguishing them from each other issuitable.

As described in the example above, Dictionary A 302 may represent a setof gestures that involve large movements of a user's limbs. DictionaryA.1 303 a (identifier=y) may be related to Dictionary A 302 in thatDictionary A.1 303 a involves large movements of the arms whereasDictionary A.2 303 b (identifier=x) involves large movements of thelegs. Dictionary B 304 may represent a set of gestures that involveprimarily finger motions, such as pinching and pointing. Dictionary B.2304 b and B.4 304 d may involve a set of gestures that assigns differentmeaning based on the number of fingers a user extends for a particulargesture. As the example demonstrates, the sub-dictionaries may be a morespecific definition for the gestures and/or additional gestures that aredefined more specifically than those in the parent dictionary.

For purposes of this example, assume that identifier y isright-handedness. Thus, Dictionary A.1 303 a in the Dictionary A 302family and Dictionary B.4 304 d may be assigned to a user that isrecognized or identified as favoring the right hand. Dictionary A.2 303b may represent the large arm movement gestures that are appropriate fora right handed user, and Dictionary B.4 304 d may represent the defaultset of small finger scale gestures appropriate for a right handed user.However, Dictionary B.2 304 b may represent gestures applicable to auser associated with identifier x and y. Identifier x may represent aparticular location in which a cultural context applies. Thus, thegestures in Dictionary B.2 304 b may represent the small finger scalegestures for a right handed user that may also be applicable within aparticular cultural context. Dictionary B.4 304 d may still be assignedto a user in addition to Dictionary B.3 304 b, but Dictionary B.2 maymodify the default gesture data in Dictionary B.4 for certain gestures.

All three dictionaries, A.1 303 a, B.4 304 d, and B.2 304 b may beassigned to a user that is right handed and is identified as having theculture represented by y. For example, there may be some gestures thatcorrespond to multiple identifiers and some gestures may be defined bymore than one dictionary family, thereby clustering dictionaries in aunique fashion for a user or a group of users. For example, the overlapof dictionaries represented by 309 comprises both large arm motion andfinger scale motion, both for a right handed user. Similarly, theoverlap of dictionaries represented by 311 may comprise finger motions,large arm motions, and be specific to a particular culture. While theremay be some gestures that are defined similarly between dictionaries,the specific manner in which they are clustered for assignment to a usermay be unique in that the gesture dictionary or combination ofdictionaries are best fit to the features associated with the user.

Referring back to FIG. 3C, at 339, if the user's gesture does not matcha dictionary gesture, the system can opt to give training to the user at350. For example, providing visual feedback representing instructionalgesture data to the user can teach the user how to properly gesture tocontrol an application. The visual feedback may be provided in anynumber of suitable ways. For example, visual feedback may be providedvia ghosted images, player avatars, or skeletal representations. In anexample embodiment, the system processes prerecorded content fordisplaying visual feedback. In another example embodiment, the systemprocesses a live feed or processes live content for displaying visualfeedback. The instructional gesture data may be in the form of correctedor highlighted feedback of a user's motion to show correct gestures orerrors in the user's motion that corresponds to a particular gesture.The feedback can portray the deltas between the user's actual positionand the ideal gesture position. The system may display feedback throughaugmented reality. For example, as described below, a display device 193may provide a contact lens view or an embedded display that allows theuser to see feedback overlaid on the user's natural physicalenvironment. The display may comprise arrows or figures to trace thatmay be aligned relative to the user's body position or movement, and thearrows or figures may provide an example of the proper position ormovement.

The system may use audio feedback, such as verbal instructions to theuser that provide the user with information regarding how to improve theuser's performance of a gesture. For example, the system could providespoken cues that indicate what the user is doing incorrectly during theperformance of a gesture. Similarly, the system may provide tactilefeedback. For example, if the user is wearing or holding a devicecapable of tactile/haptic feedback, the system may provide correctioninformation via the device. For example, a person might be wearing a pador shirt on his arm that vibrates when the arm is outside of the desiredgestural zone, or a handheld mobile device might vibrate to signal thata gesture has been done correctly.

The system may conduct a number of iterations to better understand themental model of the user and to assign the user the dictionary that mostclosely corresponds to the mental model of the user (as manifested bythe gestures rendered in response to performed functions).

If a dictionary is assigned at 340, either via explicit or passiveassignment of the gesture dictionary, the system may identify thatfurther calibration is needed at 349. The system may request additionalinputs by the user, such as at 333 and 336, or the system may passivelytrack the user as described above. The system may use the received datato further tailor the dictionary selections for the user based on inputgestures from the user. Or, the system may provide training at 350 toteach the user to gesture more closely to the gestures as they aredefined within the dictionary (or dictionaries) assigned at 340. Forexample, the gesture set calibration and assignment module 229 mayrecognize that an assigned set of gestures is not effective for certainusers interacting with the system. Rather than forcing a set of gesturedata to apply that is ineffective, the system may assign a different setof gesture data based on the data captured or alter the family ofdictionaries that are assigned to the user. For example, Dictionary A302 and A.1 303 a may still be effective, but instead of the childdictionary A.1 303 a.a, the system may reassign child dictionary A.1 303a.b.

In an example embodiment, the need for calibration at 349 may berecognized after a gesture dictionary is assigned to the user. Forexample, the gesture set may be assigned by default, such as the gestureset being deployed as part of an operating system or a gaming system, orassigned specifically assigned to a user. At 349, the need for furthercalibration may be determined by an analysis of feedback from the userby the system to determine the effectiveness of the gestures in theassigned dictionary. The gesture-based system may recognize that aparticular gesture(s) is not effective in a particular scenario andreassign the dictionary that applies. For example, the system maydetermine effectiveness by comparing the user's gesture data to gesturesdefined in the assigned dictionary. A gesture may be defined by aplurality of parameters. For example, a gesture or a portion thereof mayhave as a parameter a volume of space in which it must occur. Thisvolume of space may typically be expressed in relation to the body wherea gesture comprises body movement. For instance, a football throwinggesture for a right-handed user may be recognized only in the volume ofspace no lower than the right shoulder, and on the same side of the headas the throwing arm. It may not be necessary to define all bounds of avolume, such as with this throwing gesture, where an outer bound awayfrom the body is left undefined, and the volume extends outindefinitely, or to the edge of scene that is being monitored.

As described in more detail below, a gesture recognizer engine maycomprise information defining a gesture, such as parameters, ormetadata, for that gesture. For instance, a throw, which comprisesmotion of one of the hands from behind the rear of the body to past thefront of the body, may be implemented as a gesture comprisinginformation representing the movement of one of the hands of the userfrom behind the rear of the body to past the front of the body, as thatmovement would be captured by the depth camera. Where the gesture is athrow, a parameter may be a threshold velocity that the hand has toreach, a distance the hand must travel (either absolute, or relative tothe size of the user as a whole), and a confidence rating by therecognizer engine that the gesture occurred. Parameters for a gesturemay vary between applications, between contexts of a single application,or within one context of one application over time.

Thus, a variation between the user's gestures and the gesture data, suchas gesture parameters set in the filter for the gesture, may indicate anineffectiveness of the stored gesture data. The result may be a completefailure in the user's gesture to register with the stored gesture dataor it may result in a variation that is outside an acceptable tolerance.For example, variations between the data representative of the measuredgesture and filter parameters for a gesture(s) may indicate a failure inthe execution of the measured gesture. The variation can be compared toa threshold level of acceptance, where a variance amount that is belowthe threshold is outside the acceptable tolerance.

The system may use the failure or unacceptable variation detected as atrigger to reassign a gesture dictionary. The system may identify thecircumstances of the scenario in which the failure or unacceptablevariation occurred based on one or more identifiers recognizable to thesystem. As described herein, the identifier may be any feature of thescenario that is identifiable by the system (e.g., the operating system,current user(s), application executing, a location, etc). For example,the system may identify a location via GPS or via an IP address. Thesystem may identify a user may by capturing data representative of theuser and employing body/facial recognition techniques. The system mayidentify the operating system or an application loaded by an analysis ofthe hardware/software configuration, such as by identifying the serialnumbers of components labeled during installation.

The assignment of a gesture dictionary to a user may be doneiteratively. Consider the example of a hierarchical dictionary asindicated above. In this example, the first step of the gesturecalibration test may comprise determining whether Dictionary A 302 orDictionary B 304 is appropriate for the user. A simple gesturecalibration test may be presented to the user in which the user ispresented with feedback corresponding to an example gesture andmonitoring the gesture performed by the user. For example, assuming thatboth Dictionary A 302 and Dictionary B 304 have an exemplary gesturecorresponding to a zoom function, the system may present feedbackcorresponding to a zoom function on a display and instruct the user toperform the appropriate gesture. If the user performs a gesture thatmatches the Dictionary A 302 zoom gesture (e.g., moves both arms in apredetermined manner), the system assigns Dictionary A 302 to the user.If the user performs the gesture that matches the Dictionary B 304 zoomgesture (e.g., moves fingers in a predetermined manner and limitsmovement of arms), the system assigns Dictionary B 304 to the user. Thegesture calibration test may continue down the hierarchy to assign themost effective dictionary to the user based on repeated gesturecalibration tests. For example, if Dictionary A 302 is assigned to auser, the system may conduct additional gesture calibration tests todetermine whether Dictionary A.1 303 a or Dictionary A.2 303 b may bemore effective for a particular user

The iterative gesture calibration tests may be conducted over a shortduration of time (e.g., several tests administered in succession) orover time (e.g., not more than once per time period, such as not morethan once per day) or based on achievement (e.g., as the user reaches apredetermined threshold of gesture precision, the system could provide agesture calibration test to determine whether a more precise dictionarymay be preferable to the user).

Upon completion of a gesture calibration test and assignment, the systemmay present a tutorial, training, or other informational asset to theuser regarding the assigned dictionary. For example, the system mayprovide a display (in sequence or simultaneously) of several gesturesbeing performed for various actions. For example, there may be a displayof a user (e.g., a video of a model, an avatar, etc.) performinggestures for stop, start, select, etc. based on the assigned dictionary.This could be in the context of a game, and the gesture dictionary couldbe refined over time (consistently adapting to a more precisedictionary). For example, the system may have a collection ofdictionaries organized in a hierarchy and child dictionaries may furtherrefine a family of dictionaries such that each leg of the hierarchy isunique at a certain level.

Referring back to FIG. 2, the information in the input gesture database260 and the gesture set database 261 may be structured to enable aperson or program to extract desired information to share informationabout the user. The input gesture database 260 and the gesture setdatabase 261 may be any form of data storage, including a storagemodule, device, or memory, for example. The input gesture database 260may store input gestures for a user, compiled for comparison withgesture dictionaries to continuously update the assignment of a gesturedictionary to a user as necessary. The gesture set database 261 maystore the gesture dictionaries and identifiers and maintain theorganization of dictionaries as they relate to each other and to eachscenario. The databases 260, 261 may be provided as a databasemanagement system, an object-oriented database management system, arelational database management system (e.g. DB2, Access, etc), a filesystem, or another conventional database package. Further, the databases260, 261 can be accessed via a Structure Query Language (SQL), or othertools known to one of ordinary skill in the art.

It is noted that the gesture based system may comprise any number ofcapture devices or computing environment components. The components maybe connected locally or remotely, and a network 250 may facilitatecommunication of the various components. By connecting devices in anetwork or cloud, the gesture-based system can collect gesture data froma plurality of users, remote or local, and associate various identifiersdepending on the features of each user and/or each user's respectivecomputing environment.

In an embodiment, knowledge developed about a person on one device canbe shared across devices, such as over network 250. In an example, ifthe system has recognized a user's thresholds or preferences during theuser's first interaction with a gaming console or application, theperson may have a faster learning process when interacting with anon-gaming system. As more specific and/or different dictionaries areassigned to a user as it relates to the first device, such as the gamingconsole, the user may be assigned to a different dictionary on anotherdevice. Thus, the assignment of a dictionary on a first device may causethe assigned dictionary related to a second device to be updated. Acluster of dictionaries for a first device, for example, may be assignedto a user if the system recognizes that a cluster of dictionaries wereassigned to the user for a second device, where the cluster ofdictionaries share similar characteristics or identifiers. For example,Dictionary A 302 family may define gestures for large arm motions, andchildren Dictionaries A.1 303 a and A.2 303 b may apply in a gaming andnon-gaming and Dictionary B 304 may define gestures for small fingerscale motions. If the system recognizes that the user is assigned to theDictionary A.1 303 a family in a gaming context, the system mayinitially assign Dictionary A.2 303 b in a non-gaming context. Overtime, the system may identify that the user actually performs smallfinger scale gestures in the non-gaming context and reassign the gesturedictionaries. But, the clustering technique may more efficiently assigngesture dictionaries upon initiation.

The computing environments on the same network can share files andaccess files and settings local to another computing environment, suchas the captured gesture data. The gesture data on a local machine may beaccessed from a remote computing environment in various manners. Forexample, gesture data may be streamed over a network, such as theInternet. A web browser may be viewable on the local computingenvironment 212, and the user may browse the Internet via theinput/output component. The user may select or “click on” a gesture filethat is accessible from a server 218 to download or stream to the user'slocal machine, such as computing device 212. The gesture data may bestored by the local computing environment as a copy or back-up versionof the gesture profile that is accessible via the network. In someinstances, a temporary gesture set may be cached or otherwise storedtemporarily on a local machine. The information in the temporary gestureset may be used to refresh or add to a gesture set stored elsewhere,such as by uploading the gesture data to a central gesture database 261via the network 250.

The network 250 may be any network arranged so that messages may bepassed from one part of the network to another over any number of linksor nodes. It is contemplated that any number of links or nodes may existon the network, and any number of networks may be connected by at leastone link or node from another network. For example, the computingenvironments 212, 216, 218, 219 may each be a node on the network 250.Each computing environment 212, 216, 218, 219 may execute applicationsbut can also access other nodes (e.g., other computing environments) andapplications executing on or devices connected to other nodes, anywhereon the network 250. Thus, a user of a local computing environment mayuse the network to share data (e.g., files, databases), interact withother applications, share devices (e.g., printers), communicate withother users (e.g., email, chat), etc. For example, a user 204 ofcomputing environment 212 may access an application executing on thecomputing environment 216 via the user's local computing environment 212via the network 250. Any number of users associated with any number ofrespective local computing environments may access the same applicationvia the network 250.

There are a variety of systems, components, and network configurationsthat support networked computing environments. A network infrastructuremay enable a host of network topologies such as client/server,peer-to-peer, or hybrid architectures. The “client” is a member of aclass or group that uses the services of another class or group to whichit is not related. In computing, a client is a process, i.e., roughly aset of instructions or tasks, that requests a service provided byanother program. The client process uses the requested service withouthaving to “know” any working details about the other program or theservice itself In a client/server architecture, particularly a networkedsystem, a client is usually a computer that accesses shared networkresources provided by another computer, e.g., a server. In the exampleof FIG. 6, any computing environment 212, 216, 218, 219 can beconsidered a client, a server, or both, depending on the circumstances.

For example, the computing environment may be a server that servesseveral clients. A server 218 is typically, though not necessarily, aremote computer system accessible over a remote or local network 250,such as the Internet. The server may be the host for multi-user,multi-computing environments, providing services to clients on thenetwork 250. The client process may be active in a first computersystem, such as computing environment 212, and the server process may beactive in a second computer system, such as server 218. The gesture setidentification module 228 may assign dictionaries to users of the systemor a system local to the user may be responsible for gesture dictionaryassignment. The point is that any available processor or computingenvironment networked to the system may perform the functionalitydescribed herein. While the captured data 270 for remote users 204 a,204 b, and 204 c is provided remotely to the computing environment 218for processing, it is contemplated that a respective computingenvironment that processes captured data may be associated locally witheach of the users 204 and/or share a local environment with a capturedevice that captures data representative of each user. A gesture-basedsystem may function entirely as a unit local to a user, where a localcapture device captures data representative of the user and the localcomputing environment processes and recognizes gestures from the capturedevice for controlling aspects of the system. However, the localcomputing environment may provide the captured data and/or processeddata to a remote component of the gesture-based system. Any of the users204 may be remote to a computing environment that receives and/orprocesses the captured/processed data that represents the user or theuser's gestures. It is noted that more than one user may occupy the samephysical space, and a computing environment and/or capture device may belocally associated with more than one user.

The gesture set identification module 228 and gesture set calibrationand assignment module 229 are units representative of hardware,software, or a combination thereof that may reside on the computingenvironment 218 or another part of the gesture-based system and performthe embodiments described herein. The gesture set identification module228 and gesture set calibration and assignment module 229 are describedin this specification as modules in order to more particularly emphasizetheir implementation independence. For example, a module may beimplemented as a hardware circuit comprising custom VLSI circuits orgate arrays, off-the-shelf semiconductors such as logic chips,transistors, or other discrete components. A module may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices or thelike. Modules may also be implemented in software for execution byvarious types of processors. An identified module of executable codemay, for instance, comprise one or more physical or logical blocks ofcomputer instructions which may, for instance, be organized as anobject, procedure, or function. Nevertheless, the executables of anidentified module need not be physically located together, but maycomprise disparate instructions stored in different locations which,when joined logically together, comprise the module and achieve thestated purpose for the module.

Indeed, a module of executable code may be a single instruction, or manyinstructions, and may even be distributed over several different codesegments, among different programs, and across several memory devices.Similarly, operational data may be identified and illustrated hereinwithin modules, and may be embodied in any suitable form and organizedwithin any suitable type of data structure. The operational data may becollected as a single data set, or may be distributed over differentlocations including over different storage devices, and may exist, atleast partially, merely as electronic signals on a system or network.

As described above, the gesture set identification module 228 andgesture set calibration and assignment module 229 may be located on anend user computing environment or a host environment. Thus, theevolution of the gesture sets or data that modifies a universallyprovided gesture set may be performed or stored locally or remotely fromthe computing environment associated locally with the user. If performedlocally, a local gesture set identification module 228 and gesture setcalibration and assignment module 229 may recognize updates to gesturedata that are more effective for the users that interact directly withthe local computing environment 218. Thus, the users that interactlocally with the computing environment may benefit by an assignment ofthe gesture sets based on the users that interact directly with thecomputing environment. For a residence, for example, the gesture setsmay be assigned to reflect the gestures as they are performed by afamily that shares similar characteristics. The local users areobviously in the same region or location, but there may be furthercharacteristics that users that interact directly with each other share.For example, members of a family may make gestures in a similar fashionsimply as a result of having similar physical characteristics orinteracting in a certain personal manner.

The gesture data may correspond to various controls, such as selectfile, open file, close file, power off, load an application, etc. Foreach gesture, the system may start with a generic description of thegesture properties. As described in more detail below with respect tothe gesture recognition architecture, gesture information can includeany identifiable parameter(s) of the user's gesture, including ranges,speeds, accelerations, velocities, etc. For example, a gesture may berepresented by a trajectory of points of the user's body. As the usermoves, a trajectory representing a sequence of points of the user's bodymay be tracked between captured images. If the trajectory resembles thetrajectory defined for the gesture, or falls within an acceptable rangefor the trajectory defined for the gesture, the system may return anidentity of that gesture. For example, a baseball throwing gesture or aportion thereof may have as a parameter a volume of space in which itmust occur. This volume of space may typically be expressed in relationto the body where a gesture comprises body movement. For instance, abaseball throwing gesture for a right-handed user may be recognized onlyin the volume of space no lower than the right shoulder, and on the sameside of the head as the throwing arm.

FIG. 4 illustrates an example embodiment of the capture device 202 thatmay be used for target recognition, analysis, and tracking, where thetarget can be a user or an object. According to an example embodiment,the capture device 202 may be configured to capture video with depthinformation including a depth image that may include depth values viaany suitable technique including, for example, time-of-flight,structured light, stereo image, or the like. According to oneembodiment, the capture device 202 may organize the calculated depthinformation into “Z layers,” or layers that may be perpendicular to a Zaxis extending from the depth camera along its line of sight.

As shown in FIG. 4, the capture device 202 may include an image cameracomponent 22. According to an example embodiment, the image cameracomponent 22 may be a depth camera that may capture the depth image of ascene. The depth image may include a two-dimensional (2-D) pixel area ofthe captured scene where each pixel in the 2-D pixel area may representa depth value such as a length or distance in, for example, centimeters,millimeters, or the like of an object in the captured scene from thecamera.

As shown in FIG. 4, according to an example embodiment, the image cameracomponent 22 may include an IR light component 24, a three-dimensional(3-D) camera 26, and an RGB camera 28 that may be used to capture thedepth image of a scene. For example, in time-of-flight analysis, the IRlight component 24 of the capture device 202 may emit an infrared lightonto the scene and may then use sensors (not shown) to detect thebackscattered light from the surface of one or more targets and objectsin the scene using, for example, the 3-D camera 26 and/or the RGB camera28. In some embodiments, pulsed infrared light may be used such that thetime between an outgoing light pulse and a corresponding incoming lightpulse may be measured and used to determine a physical distance from thecapture device 202 to a particular location on the targets or objects inthe scene. Additionally, in other example embodiments, the phase of theoutgoing light wave may be compared to the phase of the incoming lightwave to determine a phase shift. The phase shift may then be used todetermine a physical distance from the capture device 202 to aparticular location on the targets or objects. The camera component 22may include other components for identifying features of a user in thephysical space. For example, an accelerometer may detect accelerationsof a user's body parts, or a gyroscope may detect an orientation of anobject the user is holding.

According to another example embodiment, time-of-flight analysis may beused to indirectly determine a physical distance from the capture device202 to a particular location on the targets or objects by analyzing theintensity of the reflected beam of light over time via varioustechniques including, for example, shuttered light pulse imaging.

In another example embodiment, the capture device 202 may use astructured light to capture depth information. In such an analysis,patterned light (i.e., light displayed as a known pattern such as gridpattern or a stripe pattern) may be projected onto the scene via, forexample, the IR light component 24. Upon striking the surface of one ormore targets or objects in the scene, the pattern may become deformed inresponse. Such a deformation of the pattern may be captured by, forexample, the 3-D camera 26 and/or the RGB camera 28 and may then beanalyzed to determine a physical distance from the capture device 202 toa particular location on the targets or objects.

According to another embodiment, the capture device 202 may include twoor more physically separated cameras that may view a scene fromdifferent angles, to obtain visual stereo data that may be resolved togenerate depth information. In another example embodiment, the capturedevice 202 may use point cloud data and target digitization techniquesto detect features of the user.

The capture device 202 may further include a microphone 30, or an arrayof microphones. The microphone 30 may include a transducer or sensorthat may receive and convert sound into an electrical signal. Accordingto one embodiment, the microphone 30 may be used to reduce feedbackbetween the capture device 202 and the computing environment 212 in thetarget recognition, analysis, and tracking system 10. Additionally, themicrophone 30 may be used to receive audio signals that may also beprovided by the user to control applications such as game applications,non-game applications, or the like that may be executed by the computingenvironment 212.

In an example embodiment, the capture device 202 may further include aprocessor 32 that may be in operative communication with the imagecamera component 22. The processor 32 may include a standardizedprocessor, a specialized processor, a microprocessor, or the like thatmay execute instructions that may include instructions for receiving thedepth image, determining whether a suitable target may be included inthe depth image, converting the suitable target into a skeletalrepresentation or model of the target, or any other suitableinstruction. For example, the computer-readable medium may comprisecomputer executable instructions for receiving data of a scene, whereinthe data includes data representative of the target in a physical space.The instructions comprise instructions for gesture profilepersonalization and gesture profile roaming, as described herein.

The capture device 202 may further include a memory component 34 thatmay store the instructions that may be executed by the processor 32,images or frames of images captured by the 3-d camera 26 or RGB camera28, or any other suitable information, images, or the like. According toan example embodiment, the memory component 34 may include random accessmemory (RAM), read only memory (ROM), cache, Flash memory, a hard disk,or any other suitable storage component. As shown in FIG. 2, in oneembodiment, the memory component 34 may be a separate component incommunication with the image capture component 22 and the processor 32.According to another embodiment, the memory component 34 may beintegrated into the processor 32 and/or the image capture component 22.

As shown in FIG. 4, the capture device 202 may be in communication withthe computing environment 212 via a communication link 36. Thecommunication link 36 may be a wired connection including, for example,a USB connection, a Fire wire connection, an Ethernet cable connection,or the like and/or a wireless connection such as a wireless 802.11b, g,a, or n connection. According to one embodiment, the computingenvironment 212 may provide a clock to the capture device 202 that maybe used to determine when to capture, for example, a scene via thecommunication link 36.

Additionally, the capture device 202 may provide the depth informationand images captured by, for example, the 3-D camera 26 and/or the RGBcamera 28, and a skeletal model that may be generated by the capturedevice 202 to the computing environment 212 via the communication link36. The computing environment 212 may then use the skeletal model, depthinformation, and captured images to, for example, control an applicationsuch as a game or word processor.

As shown, in FIG. 4, the computing environment 212 may include a memory207, having an input gesture database 260 and a gesture set database261, and a gestures recognition engine 190. The gestures recognitionengine 190 may include a collection of gesture filters 191. A filter maycomprise code and associated data that can recognize gestures orotherwise process depth, RGB, or skeletal data. Each filter 191 maycomprise information defining a gesture along with parameters, ormetadata, for that gesture. For instance, a throw, which comprisesmotion of one of the hands from behind the rear of the body to past thefront of the body, may be implemented as a gesture filter 191 comprisinginformation representing the movement of one of the hands of the userfrom behind the rear of the body to past the front of the body, as thatmovement would be captured by a depth camera. Parameters may then be setfor that gesture. Where the gesture is a throw, a parameter may be athreshold velocity that the hand has to reach, a distance the hand musttravel (either absolute, or relative to the size of the user as awhole), and a confidence rating by the recognizer engine that thegesture occurred. These parameters for the gesture may vary betweenapplications, between contexts of a single application, or within onecontext of one application over time.

While it is contemplated that the gestures recognition engine 190 mayinclude a collection of gesture filters, where a filter may comprisecode or otherwise represent a component for processing depth, RGB, orskeletal data, the use of a filter is not intended to limit the analysisto a filter. The filter is a representation of an example component orsection of code that analyzes data of a scene received by a system, andcomparing that data to base information that represents a gesture. As aresult of the analysis, the system may produce an output correspondingto whether the input data corresponds to the gesture. The baseinformation representing the gesture may be adjusted to correspond tothe recurring feature in the history of data representative of theuser's capture motion. The base information, for example, may be part ofa gesture filter as described above. But, any suitable manner foranalyzing the input data and gesture data is contemplated.

In an example embodiment, a gesture may be recognized as a trigger forthe entry into a modification mode, where a user can modify gestureparameters in the user's gesture profile. For example, a gesture filter191 may comprise information for recognizing a modification triggergesture. If the modification trigger gesture is recognized, theapplication may go into a modification mode. The modification triggergesture may vary between applications, between systems, between users,or the like. For example, the same gesture in a tennis gamingapplication may not be the same modification trigger gesture in abowling game application.

The data captured by the cameras 26, 28 and device 202 in the form ofthe skeletal model and movements associated with it may be compared tothe gesture filters 191 in the gesture set database 261 to identify whena user (as represented by the skeletal model) has performed one or moregestures. Thus, inputs to a filter such as filter 191 may comprisethings such as joint data about a user's joint position, like anglesformed by the bones that meet at the joint, RGB color data from thescene, and the rate of change of an aspect of the user. As mentioned,parameters may be set for the gesture. Outputs from a filter 191 maycomprise things such as the confidence that a given gesture is beingmade, the speed at which a gesture motion is made, and a time at whichthe gesture occurs.

The computing environment 212 may include a processor 195 that canprocess the depth image to determine what targets are in a scene, suchas a user 18 or an object in the room. This can be done, for instance,by grouping together of pixels of the depth image that share a similardistance value. The image may also be parsed to produce a skeletalrepresentation of the user, where features, such as joints and tissuesthat run between joints are identified. There exist skeletal mappingtechniques to capture a person with a depth camera and from thatdetermine various spots on that user's skeleton, joints of the hand,wrists, elbows, knees, nose, ankles, shoulders, and where the pelvismeets the spine. Other techniques include transforming the image into abody model representation of the person and transforming the image intoa mesh model representation of the person.

In an embodiment, the processing is performed on the capture device 202itself, and the raw image data of depth and color (where the capturedevice 202 comprises a 3D camera 26) values are transmitted to thecomputing environment 212 via link 36. In another embodiment, theprocessing is performed by a processor 32 coupled to the camera 402 andthen the parsed image data is sent to the computing environment 212. Instill another embodiment, both the raw image data and the parsed imagedata are sent to the computing environment 212. The computingenvironment 212 may receive the parsed image data but it may stillreceive the raw data for executing the current process or application.For instance, if an image of the scene is transmitted across a computernetwork to another user, the computing environment 212 may transmit theraw data for processing by another computing environment.

The computing environment 212 may use the gesture set database 261 alongwith a gesture profile 205 such as that shown in FIG. 2 to interpretmovements of the skeletal model and to control an application based onthe movements. The computing environment 212 can model and display arepresentation of a user, such as in the form of an avatar or a pointeron a display, such as in a display device 193. Display device 193 mayinclude a computer monitor, a television screen, or any suitable displaydevice, such as a contact lens or an embedded display. For example, acamera-controlled computer system may capture user image data anddisplay user feedback on a television screen that maps to the user'sgestures. The user feedback may be displayed as an avatar on the screensuch as shown in FIG. 1. The avatar's motion can be controlled directlyby mapping the avatar's movement to those of the user's movements. Theuser's gestures may be interpreted control certain aspects of theapplication.

The gesture data base may be locally or remotely stored on a media,e.g., a removable or non-removable media, on a computing environment,e.g., memory 207 on computing environment 212 The media can be removablestorage and/or non-removable storage including, but not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,universal serial bus (USB) compatible memory, smart cards, or any othermedium which can be used to store the desired information. The storagemedia may be implemented in any method or technology for storage ofinformation such as computer readable instructions, data structures,program modules, or other data. The

According to an example embodiment, the target may be a human target inany position such as standing or sitting, a human target with an object,two or more human targets, one or more appendages of one or more humantargets or the like that may be scanned, tracked, modeled and/orevaluated to generate a virtual screen, compare the user to one or morestored profiles and/or to store a gesture profile 205 associated withthe user in a computing environment such as computing environment 212.The gesture profile 205 may be specific to a user, application, or asystem. The gesture profile 205 may be accessible via an application orbe available system-wide, for example. The gesture profile 205 mayinclude lookup tables for loading specific user profile information. Thevirtual screen may interact with an application that may be executed bythe computing environment 212 described above with respect to FIG. 1.

The gesture profile 205 may include user identification data such as,among other things, the target's scanned or estimated body size,skeletal models, body models, voice samples or passwords, the target'sgender, the targets age, previous gestures, target limitations andstandard usage by the target of the system, such as, for example atendency to sit, left or right handedness, or a tendency to stand verynear the capture device. This information may be used to determine ifthere is a match between a target in a capture scene and one or moreusers. If there is a match, the gesture profiles 205 for the user may beloaded and, in one embodiment, may allow the system to adapt the gesturerecognition techniques to the user, or to adapt other elements of thecomputing or gaming experience according to the gesture profile 205.

One or more gesture profiles 205 may be stored in computer environment212 and used in a number of user sessions, or one or more profiles maybe created for a single session only. Users may have the option ofestablishing a profile where they may provide information to the systemsuch as a voice or body scan, age, personal preferences, right or lefthandedness, an avatar, a name or the like. Gesture profiles may also begenerated or provided for “guests” who do not provide any information tothe system beyond stepping into the capture space. A temporary personalprofile may be established for one or more guests. At the end of a guestsession, the guest gesture profile may be stored or deleted. Theinformation available in the gesture profile 205 may help the systemassign a dictionary. For example, if an identifier associated with aparticular gesture dictionary is based on a physical feature of theuser, information in the gesture profile may match the identifier, andthe system may assign the dictionary to the user based on the match.

The databases 260, 261, gestures recognition engine 190, gesture profile205, and modules 228, 229 may be implemented in hardware, software or acombination of both. For example, the gestures recognition engine 190may be implemented as software that executes on a processor, such asprocessor 195, of the computing environment 212 (or on processing unit101 of FIG. 6 or processing unit 259 of FIG. 7).

It is emphasized that the block diagrams depicted in FIG. 4 and FIGS. 6and 7 described below are exemplary and not intended to imply a specificimplementation. Thus, the processor 195 or 32 in FIG. 4, the processingunit 101 of FIG. 6, and the processing unit 259 of FIG. 7, can beimplemented as a single processor or multiple processors. Multipleprocessors can be distributed or centrally located. For example, thegesture set database 261 may be implemented as software that executes onthe processor 32 of the capture device or it may be implemented assoftware that executes on the processor 195 in the computing environment212. Any combinations of processors that are suitable for performing thetechniques disclosed herein are contemplated. Multiple processors cancommunicate wirelessly, via hard wire, or a combination thereof.

FIG. 5A depicts an example skeletal mapping of a user that may begenerated from the capture device 202. In this embodiment, a variety ofjoints and bones are identified: each hand 502, each forearm 504, eachelbow 506, each bicep 508, each shoulder 510, each hip 512, each thigh514, each knee 516, each foreleg 518, each foot 520, the head 522, thetorso 524, the top 526 and bottom 528 of the spine, and the waist 530.Where more points are tracked, additional features may be identified,such as the bones and joints of the fingers or toes, or individualfeatures of the face, such as the nose and eyes.

Through moving his body, a user may create gestures. A gesture comprisesa motion or pose by a user that may be captured as image data and parsedfor meaning. A gesture may be dynamic, comprising a motion, such asmimicking throwing a ball. A gesture may be a static pose, such asholding one's crossed forearms 504 in front of his torso 524. A gesturemay be a single movement (e.g., a jump) or a continuous gesture (e.g.,driving), and may be short in duration or long in duration (e.g.,driving for 202 minutes). A gesture may comprise a motion of the user'seyes. For example, a gesture may comprise eye movement, where the systemmay track the user's eyes and detect a particular direction or patternof movement. A gesture may also incorporate props, such as by swinging amock sword. A gesture may comprise more than one body part, such asclapping the hands 502 together, or a subtler motion, such as pursingone's lips. As described above, a gesture may comprise additionalinputs, such as voice commands or tactile inputs.

A user's gestures may be used for input in a general computing context.For instance, various motions of the hands 502 or other body parts maycorrespond to common system wide tasks such as navigate up or down in ahierarchical list, open a file, close a file, and save a file. Forinstance, a user may hold his hand with the fingers pointing up and thepalm facing the capture device 202. He may then close his fingerstowards the palm to make a fist, and this could be a gesture thatindicates that the focused window in a window-based user-interfacecomputing environment should be closed. Gestures may also be used in avideo-game-specific context, depending on the game. For instance, with adriving game, various motions of the hands 502 and feet 520 maycorrespond to steering a vehicle in a direction, shifting gears,accelerating, and breaking. Thus, a gesture may indicate a wide varietyof motions that map to a displayed user representation, and in a widevariety of applications, such as video games, text editors, wordprocessing, data management, etc.

A user may generate a gesture that corresponds to walking or running, bywalking or running in place. For example, the user may alternately liftand drop each leg 512-520 to mimic walking without moving. The systemmay parse this gesture by analyzing each hip 512 and each thigh 514. Astep may be recognized when one hip-thigh angle (as measured relative toa vertical line, wherein a standing leg has a hip-thigh angle of 0°, anda forward horizontally extended leg has a hip-thigh angle of 90°)exceeds a certain threshold relative to the other thigh. A walk or runmay be recognized after some number of consecutive steps by alternatinglegs. The time between the two most recent steps may be thought of as aperiod. After some number of periods where that threshold angle is notmet, the system may determine that the walk or running gesture hasceased.

Given a “walk or run” gesture, an application may set values forparameters associated with this gesture. These parameters may includethe above threshold angle, the number of steps required to initiate awalk or run gesture, a number of periods where no step occurs to end thegesture, and a threshold period that determines whether the gesture is awalk or a run. A fast period may correspond to a run, as the user willbe moving his legs quickly, and a slower period may correspond to awalk.

A gesture may be associated with a set of default parameters at firstthat the application may override with its own parameters. In thisscenario, an application is not forced to provide parameters, but mayinstead use a set of default parameters that allow the gesture to berecognized in the absence of application-defined parameters. Informationrelated to the gesture may be stored for purposes of pre-canned gestureanimation.

There are a variety of outputs that may be associated with the gesture.There may be a baseline “yes or no” as to whether a gesture isoccurring. There also may be a confidence level, which corresponds tothe likelihood that the user's tracked movement corresponds to thegesture. This could be a linear scale that ranges over floating pointnumbers between 0 and 1, inclusive. Wherein an application receivingthis gesture information cannot accept false-positives as input, it mayuse only those recognized gestures that have a high confidence level,such as at least 0.95. Where an application must recognize everyinstance of the gesture, even at the cost of false-positives, it may usegestures that have at least a much lower confidence level, such as thosemerely greater than 0.2. The gesture may have an output for the timebetween the two most recent steps, and where only a first step has beenregistered, this may be set to a reserved value, such as −1 (since thetime between any two steps must be positive). The gesture may also havean output for the highest thigh angle reached during the most recentstep.

Another exemplary gesture is a “heel lift jump.” In this, a user maycreate the gesture by raising his heels off the ground, but keeping histoes planted. Alternatively, the user may jump into the air where hisfeet 520 leave the ground entirely. The system may parse the skeletonfor this gesture by analyzing the angle relation of the shoulders 510,hips 512 and knees 516 to see if they are in a position of alignmentequal to standing up straight. Then these points and upper 526 and lower528 spine points may be monitored for any upward acceleration. Asufficient combination of acceleration may trigger a jump gesture. Asufficient combination of acceleration with a particular gesture maysatisfy the parameters of a transition point.

Given this “heel lift jump” gesture, an application may set values forparameters associated with this gesture. The parameters may include theabove acceleration threshold, which determines how fast some combinationof the user's shoulders 510, hips 512 and knees 516 must move upward totrigger the gesture, as well as a maximum angle of alignment between theshoulders 510, hips 512 and knees 516 at which a jump may still betriggered. The outputs may comprise a confidence level, as well as theuser's body angle at the time of the jump.

Setting parameters for a gesture based on the particulars of theapplication that will receive the gesture are important in accuratelyidentifying gestures. Properly identifying gestures and the intent of auser greatly helps in creating a positive user experience.

An application may set values for parameters associated with varioustransition points to identify the points at which to use pre-cannedanimations. Transition points may be defined by various parameters, suchas the identification of a particular gesture, a velocity, an angle of atarget or object, or any combination thereof If a transition point isdefined at least in part by the identification of a particular gesture,then properly identifying gestures assists to increase the confidencelevel that the parameters of a transition point have been met.

Another parameter to a gesture may be a distance moved. Where a user'sgestures control the actions of an avatar in a virtual environment, thatavatar may be arm's length from a ball. If the user wishes to interactwith the ball and grab it, this may require the user to extend his arm502-510 to full length while making the grab gesture. In this situation,a similar grab gesture where the user only partially extends his arm502-510 may not achieve the result of interacting with the ball.Likewise, a parameter of a transition point could be the identificationof the grab gesture, where if the user only partially extends his arm502-510, thereby not achieving the result of interacting with the ball,the user's gesture also will not meet the parameters of the transitionpoint.

A gesture or a portion thereof may have as a parameter a volume of spacein which it must occur. This volume of space may typically be expressedin relation to the body where a gesture comprises body movement. Forinstance, a football throwing gesture for a right-handed user may berecognized only in the volume of space no lower than the right shoulder510 a, and on the same side of the head 522 as the throwing arm 502a-310 a. It may not be necessary to define all bounds of a volume, suchas with this throwing gesture, where an outer bound away from the bodyis left undefined, and the volume extends out indefinitely, or to theedge of scene that is being monitored.

FIG. 5B provides further details of one exemplary embodiment of thegesture recognizer engine 190 of FIG. 2. As shown, the gesturerecognizer engine 190 may comprise at least one filter 519 to determinea gesture or gestures. A filter 519 comprises information defining agesture 526 (hereinafter referred to as a “gesture”), and may compriseat least one parameter 528, or metadata, for that gesture 526. Forinstance, a throw, which comprises motion of one of the hands frombehind the rear of the body to past the front of the body, may beimplemented as a gesture 526 comprising information representing themovement of one of the hands of the user from behind the rear of thebody to past the front of the body, as that movement would be capturedby the depth camera. Parameters 528 may then be set for that gesture526. Where the gesture 526 is a throw, a parameter 528 may be athreshold velocity that the hand has to reach, a distance the hand musttravel (either absolute, or relative to the size of the user as awhole), and a confidence rating by the recognizer engine 190 that thegesture 526 occurred. These parameters 528 for the gesture 526 may varybetween applications, between contexts of a single application, orwithin one context of one application over time.

Filters may be modular or interchangeable. In an embodiment, a filterhas a number of inputs, each of those inputs having a type, and a numberof outputs, each of those outputs having a type. In this situation, afirst filter may be replaced with a second filter that has the samenumber and types of inputs and outputs as the first filter withoutaltering any other aspect of the recognizer engine 190 architecture. Forinstance, there may be a first filter for driving that takes as inputskeletal data and outputs a confidence that the gesture 526 associatedwith the filter is occurring and an angle of steering. Where one wishesto substitute this first driving filter with a second drivingfilter—perhaps because the second driving filter is more efficient andrequires fewer processing resources—one may do so by simply replacingthe first filter with the second filter so long as the second filter hasthose same inputs and outputs—one input of skeletal data type, and twooutputs of confidence type and angle type.

A filter need not have a parameter 528. For instance, a “user height”filter that returns the user's height may not allow for any parametersthat may be tuned. An alternate “user height” filter may have tunableparameters—such as to whether to account for a user's footwear,hairstyle, headwear and posture in determining the user's height.

Inputs to a filter may comprise things such as joint data about a user'sjoint position, like angles formed by the bones that meet at the joint,RGB color data from the scene, and the rate of change of an aspect ofthe user. Outputs from a filter may comprise things such as theconfidence that a given gesture is being made, the speed at which agesture motion is made, and a time at which a gesture motion is made.

The gesture recognizer engine 190 may have a base recognizer engine 517that provides functionality to a gesture filter 519. In an embodiment,the functionality that the recognizer engine 517 implements includes aninput-over-time archive that tracks recognized gestures and other input,a Hidden Markov Model implementation (where the modeled system isassumed to be a Markov process—one where a present state encapsulatesany past state information necessary to determine a future state, so noother past state information must be maintained for this purpose—withunknown parameters, and hidden parameters are determined from theobservable data), as well as other functionality required to solveparticular instances of gesture recognition.

Filters 519 are loaded and implemented on top of the base recognizerengine 517 and can utilize services provided by the engine 517 to allfilters 519. In an embodiment, the base recognizer engine 517 processesreceived data to determine whether it meets the requirements of anyfilter 519. Since these provided services, such as parsing the input,are provided once by the base recognizer engine 517 rather than by eachfilter 519, such a service need only be processed once in a period oftime as opposed to once per filter 519 for that period, so theprocessing required to determine gestures is reduced.

An application may use the filters 519 provided by the recognizer engine190, or it may provide its own filter 519, which plugs in to the baserecognizer engine 517. Similarly, the gesture profile may plug in to thebase recognizer engine 517. In an embodiment, all filters 519 have acommon interface to enable this plug-in characteristic. Further, allfilters 519 may utilize parameters 528, so a single gesture tool asdescribed below may be used to debug and tune the entire filter system519.

These parameters 528 may be tuned for an application or a context of anapplication by a gesture tool 521. In an embodiment, the gesture tool521 comprises a plurality of sliders 523, each slider 523 correspondingto a parameter 528, as well as a pictorial representation of a body 524.As a parameter 528 is adjusted with a corresponding slider 523, the body524 may demonstrate both actions that would be recognized as the gesturewith those parameters 528 and actions that would not be recognized asthe gesture with those parameters 528, identified as such. Thisvisualization of the parameters 528 of gestures provides an effectivemeans to both debug and fine tune a gesture.

The computer executable instructions may comprise instructions forroaming a gesture profile, comprising instructions for identifying thegesture profile associated with a user, wherein the gesture profilecomprises personalized gesture information for the user, and wherein thepersonalized gesture information is derived from data captured by acapture device and representative of a user's position or motion in aphysical space; and roaming the gesture profile via a networkconnection. The instructions may further comprise instructions forreceiving a request for the gesture profile, activating the gestureprofile based on an identity of the user, and identifying the user fromprofile data.

The computer executable instructions may also comprise instructions forgesture recognition based on a user's gesture profile, includinginstructions for activating a gesture profile associated with a user,wherein the gesture profile comprises personalized gesture informationfor the user, and wherein the personalized gesture information isderived from data captured by a capture device and representative of auser's position or motion in a physical space; and recognizing a user'sgesture by comparing the received data to the personalized gestureinformation in the gesture profile.

FIG. 6 illustrates an example embodiment of a computing environment thatmay be used to interpret one or more gestures in a target recognition,analysis, and tracking system. The computing environment such as thecomputing environment 212 described above with respect to FIG. 1 may bea multimedia console 100, such as a gaming console. As shown in FIG. 6,the multimedia console 100 has a central processing unit (CPU) 101having a level 1 cache 102, a level 2 cache 104, and a flash ROM (ReadOnly Memory) 106. The level 1 cache 102 and a level 2 cache 104temporarily store data and hence reduce the number of memory accesscycles, thereby improving processing speed and throughput. The CPU 101may be provided having more than one core, and thus, additional level 1and level 2 caches 102 and 104. The flash ROM 106 may store executablecode that is loaded during an initial phase of a boot process when themultimedia console 100 is powered ON.

A graphics processing unit (GPU) 108 and a video encoder/video codec(coder/decoder) 114 form a video processing pipeline for high speed andhigh resolution graphics processing. Data is carried from the graphicsprocessing unit 108 to the video encoder/video codec 114 via a bus. Thevideo processing pipeline outputs data to an A/V (audio/video) port 140for transmission to a television or other display. A memory controller110 is connected to the GPU 108 to facilitate processor access tovarious types of memory 112, such as, but not limited to, a RAM (RandomAccess Memory).

The multimedia console 100 includes an I/O controller 120, a systemmanagement controller 122, an audio processing unit 123, a networkinterface controller 124, a first USB host controller 126, a second USBcontroller 128 and a front panel I/O subassembly 130 that are preferablyimplemented on a module 118. The USB controllers 126 and 128 serve ashosts for peripheral controllers 142(1)-142(2), a wireless adapter 148,and an external memory device 146 (e.g., flash memory, external CD/DVDROM drive, removable media, etc.). The network interface 124 and/orwireless adapter 148 provide access to a network (e.g., the Internet,home network, etc.) and may be any of a wide variety of various wired orwireless adapter components including an Ethernet card, a modem, aBluetooth module, a cable modem, and the like.

System memory 143 is provided to store application data that is loadedduring the boot process. A media drive 144 is provided and may comprisea DVD/CD drive, hard drive, or other removable media drive, etc. Themedia drive 144 may be internal or external to the multimedia console100. Application data may be accessed via the media drive 144 forexecution, playback, etc. by the multimedia console 100. The media drive144 is connected to the I/O controller 120 via a bus, such as a SerialATA bus or other high speed connection (e.g., IEEE 1394).

The system management controller 122 provides a variety of servicefunctions related to assuring availability of the multimedia console100. The audio processing unit 123 and an audio codec 132 form acorresponding audio processing pipeline with high fidelity and stereoprocessing. Audio data is carried between the audio processing unit 123and the audio codec 132 via a communication link. The audio processingpipeline outputs data to the A/V port 140 for reproduction by anexternal audio player or device having audio capabilities.

The front panel I/O subassembly 130 supports the functionality of thepower button 150 and the eject button lnposelstart152lnposelend, as wellas any LEDs (light emitting diodes) or other indicators exposed on theouter surface of the multimedia console 100. A system power supplymodule 136 provides power to the components of the multimedia console100. A fan 138 cools the circuitry within the multimedia console 100.

The CPU 101, GPU 108, memory controller 110, and various othercomponents within the multimedia console 100 are interconnected via oneor more buses, including serial and parallel buses, a memory bus, aperipheral bus, and a processor or local bus using any of a variety ofbus architectures. By way of example, such architectures can include aPeripheral Component Interconnects (PCI) bus, PCI-Express bus, etc.

When the multimedia console 100 is powered ON, application data may beloaded from the system memory 143 into memory 112 and/or caches 102, 104and executed on the CPU 101. The application may present a graphicaluser interface that provides a consistent user experience whennavigating to different media types available on the multimedia console100. In operation, applications and/or other media contained within themedia drive 144 may be launched or played from the media drive 144 toprovide additional functionalities to the multimedia console 100.

The multimedia console 100 may be operated as a standalone system bysimply connecting the system to a television or other display. In thisstandalone mode, the multimedia console 100 allows one or more users tointeract with the system, watch movies, or listen to music. However,with the integration of broadband connectivity made available throughthe network interface 124 or the wireless adapter 148, the multimediaconsole 100 may further be operated as a participant in a larger networkcommunity.

When the multimedia console 100 is powered ON, a set amount of hardwareresources are reserved for system use by the multimedia consoleoperating system. These resources may include a reservation of memory(e.g., 16 MB), CPU and GPU cycles (e.g., 5%), networking bandwidth(e.g., 8 kbs.), etc. Because these resources are reserved at system boottime, the reserved resources do not exist from the application's view.

In particular, the memory reservation preferably is large enough tocontain the launch kernel, concurrent system applications and drivers.The CPU reservation is preferably constant such that if the reserved CPUusage is not used by the system applications, an idle thread willconsume any unused cycles.

With regard to the GPU reservation, lightweight messages generated bythe system applications (e.g., pop-ups) are displayed by using a GPUinterrupt to schedule code to render popup into an overlay. The amountof memory required for an overlay depends on the overlay area size andthe overlay preferably scales with screen resolution. Where a full userinterface is used by the concurrent system application, it is preferableto use a resolution independent of application resolution. A scaler maybe used to set this resolution such that the need to change frequencyand cause a TV resynch is eliminated.

After the multimedia console 100 boots and system resources arereserved, concurrent system applications execute to provide systemfunctionalities. The system functionalities are encapsulated in a set ofsystem applications that execute within the reserved system resourcesdescribed above. The operating system kernel identifies threads that aresystem application threads versus gaming application threads. The systemapplications are preferably scheduled to run on the CPU 101 atpredetermined times and intervals in order to provide a consistentsystem resource view to the application. The scheduling is to minimizecache disruption for the gaming application running on the console.

When a concurrent system application requires audio, audio processing isscheduled asynchronously to the gaming application due to timesensitivity. A multimedia console application manager (described below)controls the gaming application audio level (e.g., mute, attenuate) whensystem applications are active.

Input devices (e.g., controllers 142(1) and 142(2)) are shared by gamingapplications and system applications. The input devices are not reservedresources, but are to be switched between system applications and thegaming application such that each will have a focus of the device. Theapplication manager preferably controls the switching of input stream,without knowledge the gaming application's knowledge and a drivermaintains state information regarding focus switches. The cameras 26, 28and capture device 202 may define additional input devices for theconsole 100.

FIG. 7 illustrates another example embodiment of a computing environment220 that may be the computing environment 212 shown in FIG. 1 used tointerpret one or more gestures in a target recognition, analysis, andtracking system. The computing system environment 220 is only oneexample of a suitable computing environment and is not intended tosuggest any limitation as to the scope of use or functionality of thepresently disclosed subject matter. Neither should the computingenvironment 220 be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary operating environment 220. In some embodiments the variousdepicted computing elements may include circuitry configured toinstantiate specific aspects of the present disclosure. For example, theterm circuitry used in the disclosure can include specialized hardwarecomponents configured to perform function(s) by firmware or switches. Inother examples embodiments the term circuitry can include a generalpurpose processing unit, memory, etc., configured by softwareinstructions that embody logic operable to perform function(s). Inexample embodiments where circuitry includes a combination of hardwareand software, an implementer may write source code embodying logic andthe source code can be compiled into machine readable code that can beprocessed by the general purpose processing unit. Since one skilled inthe art can appreciate that the state of the art has evolved to a pointwhere there is little difference between hardware, software, or acombination of hardware/software, the selection of hardware versussoftware to effectuate specific functions is a design choice left to animplementer. More specifically, one of skill in the art can appreciatethat a software process can be transformed into an equivalent hardwarestructure, and a hardware structure can itself be transformed into anequivalent software process. Thus, the selection of a hardwareimplementation versus a software implementation is one of design choiceand left to the implementer.

In FIG. 7, the computing environment 220 comprises a computer 241, whichtypically includes a variety of computer readable media. Computerreadable media can be any available media that can be accessed bycomputer 241 and includes both volatile and nonvolatile media, removableand non-removable media. The system memory 222 includes computer storagemedia in the form of volatile and/or nonvolatile memory such as readonly memory (ROM) 223 and random access memory (RAM) 261. A basicinput/output system 224 (BIOS), containing the basic routines that helpto transfer information between elements within computer 241, such asduring start-up, is typically stored in ROM 223. RAM 261 typicallycontains data and/or program modules that are immediately accessible toand/or presently being operated on by processing unit 259. By way ofexample, and not limitation, FIG. 7 illustrates operating system 225,application programs 226, other program modules 227, and program data228.

The computer 241 may also include other removable/non-removable,volatile/nonvolatile computer storage media. By way of example only,FIG. 7 illustrates a hard disk drive 238 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 239that reads from or writes to a removable, nonvolatile magnetic disk 254,and an optical disk drive 240 that reads from or writes to a removable,nonvolatile optical disk 253 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 238 is typically connectedto the system bus 221 through an non-removable memory interface such asinterface 234, and magnetic disk drive 239 and optical disk drive 240are typically connected to the system bus 221 by a removable memoryinterface, such as interface 235.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 7, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 241. In FIG. 7, for example, hard disk drive 238 is illustratedas storing operating system 258, application programs 257, other programmodules 256, and program data 255. Note that these components can eitherbe the same as or different from operating system 225, applicationprograms 226, other program modules 227, and program data 228. Operatingsystem 258, application programs 257, other program modules 256, andprogram data 255 are given different numbers here to illustrate that, ata minimum, they are different copies. A user may enter commands andinformation into the computer 241 through input devices such as akeyboard 251 and pointing device 252, commonly referred to as a mouse,trackball or touch pad. Other input devices (not shown) may include amicrophone, joystick, game pad, satellite dish, scanner, or the like.These and other input devices are often connected to the processing unit259 through a user input interface 236 that is coupled to the systembus, but may be connected by other interface and bus structures, such asa parallel port, game port or a universal serial bus (USB). The cameras26, 28 and capture device 202 may define additional input devices forthe console 100. A monitor 242 or other type of display device is alsoconnected to the system bus 221 via an interface, such as a videointerface 232. In addition to the monitor, computers may also includeother peripheral output devices such as speakers 244 and printer 243,which may be connected through a output peripheral interface 233.

The computer 241 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer246. The remote computer 246 may be a personal computer, a server, arouter, a network PC, a peer device or other common network node, andtypically includes many or all of the elements described above relativeto the computer 241, although only a memory storage device 247 has beenillustrated in FIG. 7. The logical connections depicted in FIG. 2include a local area network (LAN) 245 and a wide area network (WAN)249, but may also include other networks. Such networking environmentsare commonplace in offices, enterprise-wide computer networks, intranetsand the Internet.

When used in a LAN networking environment, the computer 241 is connectedto the LAN 245 through a network interface or adapter 237. When used ina WAN networking environment, the computer 241 typically includes amodem 250 or other means for establishing communications over the WAN249, such as the Internet. The modem 250, which may be internal orexternal, may be connected to the system bus 221 via the user inputinterface 236, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 241, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 7 illustrates remoteapplication programs 248 as residing on memory device 247. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

It should be understood that the configurations and/or approachesdescribed herein are exemplary in nature, and that these specificembodiments or examples are not to be considered limiting. The specificroutines or methods described herein may represent one or more of anynumber of processing strategies. As such, various acts illustrated maybe performed in the sequence illustrated, in other sequences, inparallel, or the like. Likewise, the order of the above-describedprocesses may be changed.

Furthermore, while the present disclosure has been described inconnection with the particular aspects, as illustrated in the variousfigures, it is understood that other similar aspects may be used ormodifications and additions may be made to the described aspects forperforming the same function of the present disclosure without deviatingtherefrom. The subject matter of the present disclosure includes allnovel and non-obvious combinations and sub-combinations of the variousprocesses, systems and configurations, and other features, functions,acts, and/or properties disclosed herein, as well as any and allequivalents thereof Thus, the methods and apparatus of the disclosedembodiments, or certain aspects or portions thereof, may take the formof program code (i.e., instructions) embodied in tangible media, such asfloppy diskettes, CD-ROMs, hard drives, or any other machine-readablestorage medium. When the program code is loaded into and executed by amachine, such as a computer, the machine becomes an apparatus configuredfor practicing the disclosed embodiments.

In addition to the specific implementations explicitly set forth herein,other aspects and implementations will be apparent to those skilled inthe art from consideration of the specification disclosed herein.Therefore, the present disclosure should not be limited to any singleaspect, but rather construed in breadth and scope in accordance with theappended claims. For example, the various procedures described hereinmay be implemented with hardware or software, or a combination of both.

What is claimed:
 1. A method for assigning a gesture dictionary, the method comprising: receiving data representative of a user in a physical space; processing the received data to identify a first motion or pose by the user that invokes an input command to a computer; correlating the first motion or pose to a first gesture dictionary of a plurality of gesture dictionaries, each gesture dictionary of the plurality of gesture dictionaries comprising a set of input commands to the computer that may be invoked by motions or poses of the user reflected in the received data; and assigning the first gesture dictionary to the user, the first gesture dictionary corresponding to the first motion or pose; and processing additional captured data with the first gesture dictionary to identify whether a second motion or pose by the user in the additional captured data invokes an input command to the computer.
 2. The method of claim 1, further comprising requesting explicit gesture feedback from the user via instructions to the user to perform a gesture.
 3. The method of claim 2, wherein the instructions comprise a display of a command and a request to the user to perform the gesture that corresponds to the command.
 4. The method of claim 1, further comprising passively tracking the user and compiling captured data for processing to identify additional motions or poses by the user.
 5. The method of claim 4, further comprising manipulating circumstances of an output to the user to elicit particular input gesture data.
 6. The method of claim 1, wherein correlating the first motion or pose to at least one of the plurality of gesture dictionaries comprises correlating the first motion or pose to a cluster of gesture dictionaries.
 7. The method of claim 1, wherein the user is a first user and correlating the first motion or pose to the at least one of the plurality of gesture dictionaries comprises: correlating the first motion or pose of the first user to an input gesture data of a second user, and assigning a gesture dictionary assigned to the second user to the first user.
 8. The method of claim 1, wherein the at least one of the plurality of gesture dictionaries is associated an identifier, wherein the identifier identifies when to implement the at least one of the plurality of gesture dictionaries.
 9. The method of claim 8, wherein the identifier is at least one of an operating system, an application, a user, a feature of a user, a location, a type of application, a hardware configuration, a software configuration, a culture, current user, geography, demography, linguistic, culture, or a style.
 10. The method of claim 1, wherein the gesture dictionary is assigned to the user in real time upon capturing the data representative of the user.
 11. The method of claim 1, the method further comprising: compiling captured data representative of the user; identifying at least one identifier associated with the user; correlating the at least one identifier to the plurality of gesture dictionaries; assigning a portion of each of the plurality of gesture dictionaries correlating to the identifier to the user, such that a combination of data from each of the plurality of gesture dictionaries is assigned to the user.
 12. The method of claim 1, further comprising: identifying a level of failure of the first motion or pose to correspond to data in the gesture dictionary, and providing instructional feedback to the user.
 13. A system for assigning a gesture dictionary, the system comprising: a memory communicatively coupled to a processor when the system is operational, the memory bearing processor-executable instructions that, when executed on the processor, cause the system: at least to: receive data representative of a user in a physical space; process the received data to identify a first motion or pose by the user that invokes an input command to a computer; correlate the first motion or pose to a first gesture dictionary of a plurality of gesture dictionaries, each gesture dictionary of the plurality of gesture dictionaries comprising a set of input commands to the computer that may be invoked by motions or poses of the user reflected in the received data; and assign the at least one of the plurality of gesture dictionaries to the user, the gesture dictionary corresponding to the first motion or pose; and process additional captured data with the first gesture dictionary to identify whether a second motion or pose by the user in the additional captured data invokes an input command to the computer.
 14. The system of claim 13, wherein the memory further bears processor-executable instructions that, when executed on the processor, cause the system: at least to: request explicit gesture feedback from the user via instructions to the user to perform a gesture.
 15. The system of claim 14, wherein the memory further bears processor-executable instructions that, when executed on the processor, cause the system: at least to: display a command on a display device and a request to the user to perform the gesture that corresponds to the command.
 16. The system of claim 13, wherein the memory further bears processor-executable instructions that, when executed on the processor, cause the system at least to: passively tracking the user and compiling captured data for processing to identify additional motions or poses by the user.
 17. A computer-readable storage device bearing computer-executable instructions that, when executed on a computer, cause the computer to perform operations comprising: receiving data representative of a user in a physical space; processing the received data to identify a first motion or pose by the user that invokes an input command to the computer; correlating the first motion or pose to a first gesture dictionary of a plurality of gesture dictionaries, each gesture dictionary of the plurality of gesture dictionaries comprising a set of input commands to the computer that may be invoked by motions or poses of the user reflected in the received data; and assigning the first gesture dictionary to the user, the first gesture dictionary corresponding to the first motion or pose; and processing additional captured data with the first gesture dictionary to identify whether a second motion or pose by the user in the additional captured data invokes an input command to the computer.
 18. The computer-readable storage device of claim 17, wherein correlating the first motion or pose to the first gesture dictionary comprises: determining whether the user is left-handed or right-handed; and wherein assigning the first gesture dictionary to the user comprises: assigning the first gesture dictionary to the user based on whether the user is left-handed or right-handed.
 19. The computer-readable storage device of claim 17, wherein capturing data representative of the user in the physical space comprises: powering down a component of the computer or a component communicatively coupled to the computer; and capturing data representative of the user performing a motion or pose to power up the component of the computer or the component communicatively coupled to the computer.
 20. The computer-readable storage device of claim 17, wherein correlating the first motion or pose to the first gesture dictionary comprises: determining a physical location or Internet Protocol (IP) address of the user; and correlating the first motion or pose and the physical location or IP address of the user to the first gesture dictionary of a plurality of gesture dictionaries.
 21. The computer-readable storage device of claim 17, wherein correlating the first motion or pose to the first gesture dictionary comprises: determining a skill level of the user; and correlating the first motion or pose and the skill level of the user to the first gesture dictionary of a plurality of gesture dictionaries. 